Putting soil nutrient budgets into practice

Transcripción

Putting soil nutrient budgets into practice: policy and extension
education products based on research with smallholder farmers
Final Project Report to the McKnight Foundation
December 2008
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3,000
Soil Total N
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Table of Contents:
1.
2.
3.
4.
5.
6.
7.
8.
9.
Executive summary
Concept Map of project objectives
Activities carried out with project funding
Outcome 1: Nutrient Budgeting tool
Outcome 2: Simulation model of long-term nutrient dynamics in
Northern Potosi
Outcome 3: table-top and computer game to teach about nutrient
management and the importance of reducing erosion
Outcome 4: Policy brief for local municipal authorities.
Outcome 5: Materials on macronutrients in crops and soils for the
Andean CoP.
Appendices
Project participants:
Laurie Drinkwater, Principal Investigator
Steven Vanek, graduate fellow and report author
Charles Nicholson, modeling consultant
Luning Wang, budgeting tool programmer
Bhuvan Singla, game programmer
Meagan Schipanski, assistance in development of nutrient budgeting tool
Many thanks to:
Collaborating farmers, students in AgSci 3800 at Cornell University, Humberto
Beingolea, Sergio Larrea, Carlos Medrano, Ramiro Aguilar, Cirilo Cusi, Freddy
Oporto, Mario Ajurachi, Pedro Flores, Romulo Aguilar, Alejandro Mamani, Pedro
Mamani, Vitalio Mareño, Stephanie Meik, Graeme Bailey, Cornell Computer
Science Department M.Eng program.
Front cover credits: Top right: Sósimo Tomás of Sicoya, San Pedro de Buena Vista Municipality,
with his family’s goats during sampling for the nutrient budgeting project.
Middle left: Farmers from Kisivillque, Sacaca Municipality play the farmer nutrient management
game with World Neighbors staff in July 2008. Game photo by Peter Berti, other photos by Steven
Vanek.
1
Executive summary
In our project proposal we stated that the overall goal of our one-year project was to make
modeling and simulation approaches more accessible and usable for farmers, development
professionals, researchers, or policymakers. Today, our outcomes are tools and games for analysis
and learning that will be distributed via McKnight’s Andean Community of Practice. As a side
benefit, they have also had substantial impact in the educational work taken on by the Drinkwater
lab at Cornell University to benefit farmers and extensionists in the United States. Our detailed
objectives appear below in figure 1, reproduced from our midterm report. As an executive
summary we describe our outcomes, impacts, and conclusions.
Outcomes, impacts, and conclusions:
1. Computer Nutrient Budgeting Tool: We developed a computer program for farmers,
development professionals, and researchers that allows simple accounting and graphing of
nutrient trends in soils when fertility management data is available or when a user wishes to
compare imagined scenarios. The tool is designed as a way that farmers and those who work
with them can quickly decide whether management is depleting or enriching the soil with the
major crop nutrients: nitrogen (N), phosphorus (P), and potassium (K). We employed our
sampling of crops in the Andean region of Northern Potosí to design the budgeting tool. The
program is a Java application downloadable from the McKnight website
(http://mcknight.ccrp.cornell.edu/projects/AND_soil_nutrients/soil_nutrients_project.html).
The approach received positive initial evaluation from a panel of extensionists at Penn State
University and positive feedback at the July CoP 3 Meeting in Cochabamba, Bolivia. It
requires further field testing with professionals in the Andean Region. Pending the availability
of a computer science Masters student, we will be making additional improvements to the tool
in Spring 2009
2. Dynamic Simulation Model of nutrient management by smallholder farmers in Northern
Potosí, Bolivia. We combined our sampled data on nutrient management and erosion in
Northern Potosi with other information about nutrient flows in cropping systems to build a
dynamic simulation model in the Vensim modeling environment (Ventana systems, Harvard,
MA, http://www.vensim.com). The model estimates nutrient levels of N, P, and K in a farmed
field for a 50-year time period. A first major finding was that in the absence of soil erosion, soil
nutrient management is close to maintaining soil nutrient levels in a steady state, with the
exception of soil mining of potassium because of crop biomass exports with harvest such as
potatoes, fava beans, and corn stover. This is different from some areas of the developing
world, where crop management alone is responsible for rapid declines in soil nutrient stocks
because harvested crop nutrients are never replenished.
In sensitivity analyses of the model we were able to identify soil erosion rates and
manure application rates as the dominant drivers of soil nutrient sustainability. Because these
are dominant drivers, it is important to measure or model these more precisely in the future, and
understand the social, economic, or biophysical factors (like climate or elevation) on which they
depend. Meanwhile, factors such as crop nutrient content or nitrogen losses to denitrification
and leaching were weak drivers of model behavior, and would receive a lower priority in future
research. Understanding the factors that affect crop yield, especially the way that yields depend
on the internal cycling and fractions of nutrients in soil, are probably important for improving
the model, or if the dominant driver of soil erosion is reduced so that other drivers become more
2
significant. Since manure application rate is an important driver, understanding the
sustainability of animal grazing and the use of rangeland is an important future dimension for
research.
Using the model to test different scenarios, we again saw that reducing erosion is an
essential part of any program to promote sustainable management in regions like Northern
Potosí. Legume green manures could provide additional nitrogen, but if they completely
replaced manures and other fertility inputs they will lead to shortages of P and K. Retaining all
residues of crops on fields would reverse the soil mining of K that characterizes the system, and
might also reduce erosion, but this change is unlikely if residues continue to be used as animal
fodder.
3. A nutrient management game in table-top (non-computer) and computer animated versions,
to teach lessons derived from the research and the simulation modeling. The games are based
on a similar structuring of a round of play and with similar incentives, tradeoffs, and take-home
messages. We aim that the game should change attitudes and practices of farmers, especially as
regards their valuation of efforts at soil conservation like contour barriers, terracing, or residue
retention on fields. The table-top game provides a more tactile experience of moving beans into
and out of fields to represent nutrients, and is suited for workshop situations where two or three
teams can play under the guidance of a facilitator. In addition to understanding the take-home
messages about the importance of managing erosion, positive evaluations of the workshop game
by Bolivian farmers, Cornell students, and researchers all cited the usefulness of seeing and
experiencing the process of soil nutrient management in a visual, tactile way. We have
provided a short video of one of the test games with researchers at Cornell on a DVD to
accompany this report.
Meanwhile, the computer adaptation can accommodate greater complexity of the rule set,
more fields, animated sequences, and educational content accessible to the player by clicking on
tutorial buttons. The computer version is designed as a proof of concept for other such efforts
to combine information technology with extension efforts for farmers. It will require some
additional work and field testing to make all the messages completely transparent, especially for
non-literate audiences. In Bolivia, World Neighbors is interested in bringing the table-top game
to market, while the computer game may receive an initial trial with a rural laptops initiative in
Peru (Put into contact with us by via Claire Nicklin). We will also field test it during the trip to
Bolivia in January 2009, and continue to look for ways to improve it through testing at Cornell.
4. A policy brief to be presented to local government authorities and those influencing policy on
soil management and agriculture in Bolivia. This will be authored with World Neighbors for
presentation to local authorities in January 2009. Much of it will be abstracted from the full text
of the report below. By way of World Neighbors we hope to make it available to the Bolivian
plataforma nacional de suelos (national soil network), a consortium of organizations that will
have influence on soil policy in Bolivia.
5. Materials on soil nutrient management for the Andean Community of Practice. These were
sent to the listserv and have been posted on the McKnight website
They have been used by World Neighbors in Ecuador and Bolivia as training materials for
farmers, farmer field workers, and technical staff. In addition we returned our nutrient sampling
data to farmers in the form of charts that show amount of nutrients extracted by crop harvests of
common Andean crops. An example of these charts is presented as the final appendix.
3
Figure 1 (reproduced from the midterm report). Concept map showing the relationships between the four nutrient budget modeling
deliverables of the project grouped into two outcome areas for two different ‘audiences’ or beneficiary groups. Arrows are labeled with the way
that the different deliverables support each other.
Outcome area 1: Andean community of practice
Farmer simulation game
Main objective: easily understood messages
from nutrient budgeting of local farms in a fun
game setting
can build
understanding
towards
Auxiliary objectives: provide feedback/critique
of farmers to underlying model.
is the basis for
Allows projection of likely impacts to nutrient status of
changes in management
corrects and
validates
Dynamic Simulation Model
Main objective: Underlying, more complex
model designed to incorporate increasing levels
of detail in sub-models and spatial scale and
answer research questions or guide research.
Auxiliary objectives: allows identification of
priority nutrient flows for improved management
Allows projection of likely impacts of changes in
management by farmers
Format for research on the impact of farmer
wealth, regional differences in management,
elevation, climatic risk, livestock management,
or soil fertility
Auxiliary objectives: provide feedback/critique of
extensionists and farmers to underlying model.
Motivation for end users to carry out additional
sampling to refine model
Framework for educating on roles of and
limitations from major crop nutrients
corrects and validates
Nutrient budgeting tool
Main objective: Readily understood analysis of
likely trends in soil fertility given actual local practices
of soil nutrient management.
is simplified to
can be used to
demonstrate
Outcome area 2:
Local policymakers
is the basis for
Policy Brief
Main objective: easily understood
and relevant messages for
policymakers.
Additional: provides working
framework for targeting possible
policy changes
4
Introduction:
In this final report for the project we present the principal project outcomes and
explain what we have learned from each. We’ll also outline our expectations for
where the outcomes can be applied in the field and the impacts we’d hope for from
these policy and extension products. In addition, our final outcome of a policy brief is
still in progress and will be presented to local municipal leaders and authorities in a
trip to the region during January 2009.
Project activities:
1. Measurement of crop yields in highland Bolivia using direct, in-field techniques,
and sampling of crops for drying and nutrient analysis. Steven Vanek has
sampled crops, manures, and soils throughout his dissertation fieldwork with
World Neighbors (WN), and funding under this project has allowed him to
continue this process and pay for analysis of plant samples.
2. Developing a model for dynamics of nutrient stocks in farmed fields. Working
in conjunction with Chuck Nicholson, a modeler at Cornell, we have developed
a quantitative stock and flow model of nitrogen (N), phosphorus (P) and
potassium (K) stocks in farmed fields of the project area, using the Vensim
modeling environment. Our model was used for sensitivity analyses and also as
a predictive tool for the medium and long-term changes in soil nutrient status. It
is a first step towards a more integrated model to include livestock, rangeland,
and aggregation of single fields into sets that represent an entire community.
3. Development of a computer-based nutrient budgeting tool in both Spanish and
English versions that allows users to calculate and visualize budgets for typical
management scenarios in the Andean region and the temperate zone. This leads
to simple and useful information about whether crop rotations are tending to
deplete or enrich soils over the medium to long term. This product was
developed through collaboration of our project with a Masters of Science student
in the computer science program at Cornell.
4. Development and testing of a nutrient management game in Bolivia and at
Cornell University. We developed a ‘table-top’ game using beans and board
representations of fields that increases farmer knowledge of the three major crop
nutrients in soils and allows them to recognize and respond to the threat of soil
erosion to crop production and livelihoods. We developed a first version of the
game that we tested in Northern Potosi after the CoP meeting in July 2008, and
during fall 2008 at Cornell.
5. Computer simulation game of farm field management with simple animation.
As in the case of the nutrient budgeting tool, we worked with a M.Eng computer
science student at Cornell to design a computer version of the nutrient
management game for the Windows operating system. This game is currently in
the final phases of development and translation into Spanish for use in
workshops by NGO staff in Bolivia or other Andean regions, and a working
prototype is included with the report materials.
6. Presenting outcomes and work on modeling at conferences and meetings: CoP4
in Cochabamba, Bolivia, July 2008, and an extension meeting at Penn State in
January 2008.
5
Outcome 1: computer nutrient budgeting tool
In fall 2007 we worked with Luning Wang, a computer science MEng student at
Cornell, to develop a computer tool that allows tracking of soil nutrient balances for a variety of
farming systems. We chose this deliverable as our first collaboration with the Masters of
Engineering program because it allowed us to immediately deliver a product from a large base
of data on crops and organic soil amendments such as manures, composts, and green manures
compiled by the Drinkwater lab group.
Meagan Schipanski, Luning Wang, and Steven Vanek collaborated on designing and
implementing the nutrient budgeting tool. This included a great deal of student programming
work by Luning Wang, for which we are grateful. The tool allows users to generate a simulated
crop rotation of up to ten years duration, selecting crops and the manures, green manures,
fertilizers, or other amendments used to grow them. The tool makes a graph showing the
ongoing nutrient balance, or exports minus inputs, for the years where data has been entered.
This allows an extensionist or farmer to assess the trends in fertility likely to result from his or
her crop management. The tool allows farmers to see whether their management will deplete
the soil, maintain nutrients in balance, or increase soil nutrients in ways that might either be
necessary or harmful depending on whether soil is depleted at the outset. To better reflect the
actual conditions in cropping systems and the simulation model we are developing, we also
added estimates of soil nutrient losses through erosion, leaching of soils, and denitrification.
We have released the budgeter as an executable with Spanish and English versions,
downloadable from the McKnight website
The executable is a Java application and requires the installation of free Java software, available
directly from Java or on a CD we provide. Figure 2 shows a screenshot of the nutrient budgeter
from the help file, which is included as an appendix below for those who wish to learn more
about this software.
6
Figure 2: Screenshot from the nutrient budgeting tool software, showing areas for entering amendments
and green manures, harvested crops and exports, and graphical and numerical summaries of balances
for 10 consecutive simulated years.
Because the tool is being developed in both Spanish and English, we also have the
opportunity of field-testing it in the United States with organic farmers who manage soil
fertility with manures, composts, and other inputs similar to those in the Bolivian context. We
were able to demonstrate the previous version of this budgeting tool, an excel spreadsheet, to
extensionists at Penn State University in January 2008. Extensionists had worked with farmers
around Pennsylvania to enter farm data into our previous budgeting tool and examine the
lessons that emerged about soil nutrient management. Among three such tools tested by
extension workers, ours received the most positive evaluations for the ease of use. These
comments validated our work and we believe reflects the need for simple models and training
games that are sufficiently accurate to deliver key messages for farmers managing soil fertility.
Extensionists at the Penn State evaluation session also told us that the tool was best used
by farmers in conjunction with extensionists, the latter acting as interpreters and facilitators. In
their experience testing such budgeting tools, not all farmers may be accustomed to considering
soil nutrient management as a balance over the timescale of a crop rotation. These
extensionists felt that the best use for the tool was to develop illustrative case studies based on
two or three farmers in an area. These case studies could then be explained to a larger group of
farmers to teach about regional problems and solutions in nutrient management. We believe
this is an excellent recommendation because it applies equally to vegetable farmers in
Pennsylvania facing problems of nutrient overloading and pollution in their soils, or farmers in
Bolivia who face dramatic challenges with soil erosion that causes lowered yields and increased
vulnerability to risks.
7
Outcome 2: dynamic simulation model
We have developed a model (see appendix for full diagram) in the VenSim modeling
environment that allows simulation of nutrient stocks in soils (N, P, and K) on a single field
over a 50-yr time period. This allows us to measure trends in soil fertility that we would predict
from current practices and rates of soil erosion, and under a variety of alternative scenarios. As
in the case of the nutrient budgeting tool, we are applying a ‘mass-balance’ approach typical in
ecosystem ecology, in which we use measured values or estimates to model all inputs and
exports to the system. This approach describes little of the internal cycling of nutrients in soil
(which is poorly constrained for soils in the Andes anyway) and is not useful for objectives like
precisely predicting the nutrient availability to crops in the next year. Rather, the model is
strong in predicting medium to long-term declines or increases in total soil nutrient levels, such
as the timescale of 50 years that we chose for all model runs in the graphs below.
<residue removal
switch>
Fava bean yield
residue adjustment- N
<fava bean yield
f(Von liebig)>
Fava
Bean DM
N
content
Wheat yield residue
adjustment - N
<zero dummy switch>
Wheat
DM N
content
N in fava bean harvest
ize yield residue
djustment - N
ntent
nt
potato
FW N
content
kg/ha> Lookup for Maize
Response to N
ield
g/ha>
lookup for fava bean
response to N
Lookup for Wheat
Response to N
<wheat y
f(Von lieb
fallow vegetation
fixed N
<Fava
c
field 1 - N
<potato yield
f(Von liebig)>
Biological N fixation
N in potato harv est
<Initial Level of N
in Field>
reference
leaching rate
lookup for potato
g/ha> response to N
<amt fixed N in
fallow biomass>
N in wheat harvest
N in Maize harvest
Maize
DM N
content
s
N leaching
reference
denitrification rate
Potato yield
f(Ncurrent)
<pulsetrain
combined>
Maize yield
f(Ncurrent)
fava bean yield
f(Ncurrent)
Wheat yield
f(Ncurrent)
Tarwi green manure N
Manure application - N
denitrification
leaching/
denitrification switch
Ratio of Current
to Initial N
Initial Level of N
in Field
%N deri
atmos
<manure N
application>
erosion N
flow
lookup for erosion
rate of N
reference soil N
content
<manure P
application>
m
appl
lookup for erosion
rate of P
reference soil total
flow of applied m
P content
field 1 -
Figure 3: Partial view of the Nitrogen portion of the dynamic simulation model, showing the
inflows and outflows of N from a modeled field, calculated at a half-year time-step. For full
diagram see the appendix and the model in the supporting materials
8
Building the model:
To construct the model, we gathered data on all the input and output flows for farmed fields in
the area. We measured nutrient flows from our crop harvest sampling in the World Neighbors
project area (Table 1) and made measurements of erosion rates on farmers’ fields to confirm the
rates from the simple RUSLE soil erosion model (Revised Universal Soil Loss Equation,
Renard et al. 1994) we are using (Fig. 4 below). The rates we measured and the modeled rates
are also roughly in accordance with similar rates modeled in the Andes or gathered from
literature for a variety of locations and cropping systems (Leon 2005, Montgomery 2007). We
used experimental data from our research on legume green manures in the area to calculate
amounts of nitrogen fixation in legume crops and green manures. We estimated gaseous and
leaching losses of nitrogen from other data and consultation with other modelers. We also
show below that these leaching and denitrification flows likely made little difference to the
behavior of the model.
Table 1. Values used in the model for nutrient contents of harvested portions of crops Nutrient
Budgets (each sampled field = 3 samples). Data was also taken on harvested crop residues for
wheat, maize, and fava beans (not shown) :
Crop
Number of
fields (n)
Mean
Std Error
Range (min-max)
Potato
36
Dry Matter Content1 (%)
26
0.5
Potato
Fava Bean
Maize
Wheat/Barley
36
6
17
17
N content in dry matter (%)
1.03
0.04
4.07
0.03
1.02
0.06
1.57
0.05
0.67-1.45
3.95-4.15
0.80-1.42
1.21-2.11
Potato
Fava Bean
Maize
Wheat/Barley
36
6
17
17
P content in dry matter (%)
0.16
0.01
0.44
0.07
0.30
0.02
0.34
0.01
0.10-0.25
0.27-0.64
0.24-0.37
0.23-0.45
36
6
17
17
K content in dry matter (%)
1.85
0.04
1.31
0.07
0.49
0.02
0.49
0.02
1.43-2.31
1.14-1.59
0.42-0.59
0.35-0.63
Potato
Fava Bean
Maize
Wheat/Barley
18 - 30
1
Dry matter content is shown only for potato; other crops are harvested dry, and dry matter content is assumed to be
approximately 100%
9
150
125
Erosion
rate (Metric
tons/ha/yr)
100
75
50
25
0
small grain
fallow
Figure 4. Measured annual erosion rates on cropped small grain (wheat and barley) fields and
fallow fields in Northern Potosí that were used to check the erosion predictions in the model and
adjust climate erosivity and soil erodibility factors of the RUSLE soil erosion model. Error bars
are +/- 1 std error, with n=7 fields for each type of field:
As is usually done in dynamic simulation models, we implemented feedback loops that
embody the known behavior of soil-crop systems: for example, as nutrient levels go down,
crop yields go down, as does nutrient export by erosion. The precise nature of these feedback
relationships is not known, but defining a rough suspected relationship is likely adequate
because agronomic knowledge constrains the way that crop yields can behave as soil fertility
declines: whether crop yields decline is not in question, only the particular response that might
occur. Sensitivity analyses can also tell us to what degree our conclusions from the model
depend on this response curve of crops and soils to erosion, which is the dominant driver of
nutrient decline or increase. The sensitivity analysis also allows us to recommend areas of
worthwhile investment in improving models such as this for the Andes.
In our model we chose to model the total quantities of N and P in the model field, while
choosing to model only exchangeable (crop-available) potassium (K). This is because our soil
analyses revealed that the quantities of N, P, and exchangeable K are roughly of the same order
of magnitude for the soils of Northern Potosi, whereas the total K of young soils like those in
the project area can be enormous and contains a large fraction of unavailable K. If the larger
total soil K amounts were used in the model, it would mean that export of K by crops would
under-represent the real impact of this export on available K in soil. This is true to a lesser
extent for P and N, which is why we used total P and N amounts for these two nutrients. We
had hoped to apply a published sub-model for P developed from long-term experiments that
would have described internal soil pools of P (Karpinets 2004), but there were significant
methodological problems that led us to doubt the accuracy of this model, which we will not
describe here. However, below we present a ‘thought experiment’ for P cycling where we
conclude that the broadest conclusions of our modeling would be unaffected by improved
understanding of P dynamics in soil. Nevertheless, improved understanding of P dynamics in
Andean soils would help with finer aspects of better determining crop yields in the model, and
is always a welcome line of scientific inquiry. The relative insensitivity of the model to internal
P dynamics is largely due to the dominance of soil erosion, manure application, and crop
harvests as major drivers of the model. In the final analysis, using total N and P, and
10
exchangeable K, allows us to be most certain of our conclusions: for example, manures applied
to soil add P and N to pools of differing availability in a complex and unknown way, but we are
certain that they add to the total pools of these nutrients. In contrast, we are also certain that
manure K goes almost entirely to the exchangeable pool of K rather than fractionating between
available and unavailable forms.
Reference mode behavior of the model:
The ‘reference mode’ of our dynamic simulation model is expected behavior for the real system
that is generated by a sufficiently accurate representation of the different stocks and flows
(Figs. 5 a-c). Soil erosion affects the modeled behavior so strongly that it is useful to split this
model output into five separate prediction curves based on percent slope of the modeled field,
from flat (zero erosion) to a 30% slope where modeled erosion losses exceed 200 metric tons/
ha/yr. The modeled rotation was Potato-maize-fava bean-wheat-fallow-fallow. We assumed
the average manure application rate from our sampling of 9 Metric tons (Mg)/ ha in the potato
cropping year, and a small amount of legume fixed N from fallow vegetation during two years
of fallow. The oscillations that are superimposed on the declining curves result from the
application of manure and incorporation of fallow vegetation at the beginning of each rotational
cycle.
The starting value of 2000 kg/ha as the field stock for all nutrients is both a rough
estimate of the real stocks and a choice made to standardize the starting point of the model.
This figure is correct as a medium to high value, to within an order of magnitude, based on our
results for total N and P and exchangeable K in soils from Northern Potosí. By starting with a
medium to high value we are able to approximate the situation of land recently converted to
agriculture and then examine trends as nutrients are extracted by erosion and crop harvests. We
do not pretend to know the exact starting stock of the nutrient, because the focus of the model is
on estimated trends rather than the exact values.
11
5a. Soil total N versus time for 50 years
p ofp modeled rotation
3,000
Soil slope
0% (flat)
2%
2,000
5%
8%
1,000
16%
30%
0
5b. Soil total P versus time for 50 years of modeled rotation
3,000
Soil slope
0% (flat)
2%
2,000
5%
8%
1,000
16%
30%
0
5c. Soil exchangeable K versus time
p for
p 50 years of modeled rotation
3,000
Soil slope
0% (flat)
2%
5%
8%
16%
2,000
1,000
30%
0
Years:
10
20
30
40
50
Fig 5 a-c: Shows modeled total nutrient levels at average manure applications and for typical crop
rotations over 50 years. Crop rotation is potato-maize-fava bean-wheat-fallow-fallow, with 9 Metric
tons/ha manure (dry wt.) of average sampled composition, applied every six years in the potato
cropping year. Soil slopes chosen for modeling reflect FAO slope classes: 0-2%, 2-5%, 5-8%, etc.
All graphs start with 2000 kg·ha-1 as a rough order of magnitude estimate of soil nutrient stocks
that standardizes the outcomes of the model to provide easy comparison.
12
The different curves for different erosion rates show a number of interesting features:
• Even though randomization in erosivity (i.e., how damaging a year was in terms of
erosion) and crop yields was carried out to simulate the way that rain events and crop
risk from pests or weather events can vary from year to year, the behavior of N, P and K
stocks in the model behaves in a stable way. Oscillations from manure applications and
crop export are superimposed on a set of curves ranging from steep declines to very
modest increases. Stability of behavior is a good sign for the rigor of the model within
the limited prediction role we have given it. On the less positive side in the real world,
such stability indicates that large changes in practices or conditions will likely be
needed to change the downward trends shown for real cropping systems.
• In the absence of erosion, the nutrient management being practiced by farmers in
Northern Potosí is would likely lead to steady levels of N and P, while tending over the
long term to slowly deplete soil K. This stands in contrast to other areas of the
developing world, where chronic deficits in soil nutrients result solely from cropping
(soil mining) without taking soil erosion into account.
• Soil K deficits are likely the result of the presence of potatoes and fava beans as major
crops as well as the export of residues for animal feed (see table 1 for the tissue
concentration differences between potato, fava beans, and grains). Corn stover, used to
feed cattle, was found to export relatively large amounts of potassium.
Sensitivity analyses of the model:
These analyses test how sensitive the model is to changes in given input variables. Parameters
to which the model is most sensitive are usually the most deserving of further research to
increase precision of estimates or measurements, so that the precision of model outputs like the
N, P, and K levels seen here can be increased. Sensitive parameters might also be the ones
where we seek greater explanatory power, by asking questions like ‘does manure application
rate depend on wealth level?’. Here we demonstrate five sensitivity analyses of our model and
the conclusions we drew from each one. For parameters based on our sampling work we varied
each parameter to plus/minus three standard errors of its measured value in the model, so that
we covered >95% of the likely variation in the parameter. For sub-model inputs like rainfall
erosivity in the RUSLE soil erosion model we sometimes had limited actual data for predictive
parameters, and so we estimated the entire range of conditions for farmed fields in the WN
project area. Each graph below shows a number of bands that bracket the mean performance of
the model, with different confidence levels assigned to each band. The outer two bands (95%
and 100% confidence) are the most interesting for our purposes, since they determine the range
of possible behaviors for the model.
1. Sensitivity to erosion inputs (slope, slope length, erosivity of rainfall, cover and
management factors). Figure 6 shows a sensitivity analysis for all three nutrients responding to
factors that influence the erosion rate. The wide band around the average behavior of the model
indicates the same thing as the wide range in the shape of curves above that describe different
soil slopes: the model is very responsive to erosion, and thus sensitive to uncertainties in the
factors that determine erosion rate. In figure 7, the sensitivity analysis for potato yields shows
the same degree of impact from uncertainties in factors determining the erosion rate. High
sensitivity for these parameters argues for the use of more precise erosion models and more
measurements of erosion in the actual project area to improve the understanding of erosion, so
as to assess whether the curves in Fig. 5 a-c paint a realistic picture of the nutrient losses that
are actually occurring in the farming system. In particular, the erosivity of the climate or
amount of energy from rainstorms through the year that acts to erode soils is known with low
13
precision for the Andean region. Our estimate for erosivity of climate was developed by
borrowing erosivity values from a part of the United States that has an analogous climate (dry
Great Plains and the Mediterranean northwest). Although erosion models and measurements
always retain some imprecision, a recent work on a watershed in Peru (Leon 2005) uses a new
event-based erosion model called WEPP (Water Erosion Prediction Project) to improve
estimates of erosion at the scale of a watershed. Event-based models differ from the empirical,
yearly-based model we used (RUSLE) in that they use the erosive energy of each rain event to
predict erosion. This feature allows the use of actual weather data to predict erosion year by
year. The presence of these models and the need to calibrate them is a strong argument for
better weather instrumentation on the ground and ‘mining’ of existing data in the Andean
region to determine rainfall amounts on either a season-by-season
or storm-by-storm basis.
p _se s
Confidence level bands for sensitivity analysis graphs:
4,000
50%
95%
100%
4,000
4,000
Soil P
Soil N
75%
Soil exch. K
3,000
3,000
3,000
2,000
2,000
2,000
1,000
1,000
1,000
0
0
0
Figure 6: Sensitivity analysis of soil nutrient stocks to changes in factors governing the erosion
rate. Analysis shows high sensitivity in agreement with Fig. 5 above. Different color bands show
different confidence intervals from 200 runs of the model; the 95% confidence interval is likely the
best for interpretation. Each graph uses a horizontally compressed x-axis of 50-yrs.
20,000
15,000
10,000
5,000
0
Years:
10
20
30
40
50
Figure 7. Sensitivity analysis of potato yields in the model, responding to factors influencing the
erosion rate. The starting base yield was the average yield measured of 12.7 metric tons per hectare.
2. Sensitivity to changes and uncertainties in manure application rate and nutrient content.
When we vary manure rates and nutrient contents over their full sampled ranges, we get the
sensitivity analysis below for soil N, P, and K (Fig. 8) and for potato yield in kg/ha (Fig. 9).
14
The low range in variation in soil P is likely due to the low amounts and low variance of P
content in manure relative to the other two nutrients. The opposite is true for K, where higher
concentrations and high variability between different farms’ and communities’ manure led to
widely varying model runs, including about 25% of runs with a net positive balance
(>2000kg/ha K at end of the analysis). Meanwhile, in this sensitivity analysis it is declining
soil P with low variability that drives the modeled declines in potato yields to show substantial
declines with high confidence (narrow confidence bands). The declining P indicates that
erosion removes large quantities of P that cannot be replaced by manure, so that in the model it
becomes the limiting nutrient for crop growth. This leads to an interesting hypothesis: since
legumes are known to require relatively high levels of P for adequate production: could the
relative lack of legumes in rotations found in the World Neighbors baseline data be a result of
this inevitable P impoverishment of soils due to erosion, rather than just a cultural or
educational phenomenon, as NGOs sometimes assume? For future model development, the
manure sensitivity analysis also indicates that measuring manure application rate and nutrient
content is an important part of calibrating the model for a particular community or region.
4,000
4,000
4,000
Soil N
3,000
3,000
Soil P
Soil exch. K
3,000
2,000
2,000
2,000
1,000
1,000
1,000
0
0
0
Fig. 8. Sensitivity analysis of manure application rate and nutrient content on soil N, P, and
exchangeable K levels. The central line of the sensitivity analyses is the models behavior with a
12% soil slope and 9 Mg/ha applied manure at the average nutrient concentration.
20,000
15,000
10,000
5,000
0
Fig. 9. Sensitivity of potato yield to changes in manure application rate. The modeled potato yield
is likely constrained in its variation (narrower band than for erosion above, for example) by the low
variation in manure P content (fig.8, middle graph) and the fact that the model uses a most limiting
nutrient method (Von Liebig’s law of the minimum) to calculate yields.
15
3. Sensitivity to changes in nutrient content of harvested portions of crops: in a third
sensitivity analysis, we varied the dry matter content (for potatoes) and nutrient content of crops
across virtually their entire range of sampled values (Table 1; varied across +/- 3 std. errors).
The graphs for this sensitivity analysis (fig. 10) show that the uncertainties in these values
would have very little effect on the interpretations of nutrient balances. This is a helpful result
because it means that the nutrient budgeting methods can be adapted to other regions within the
Andes or elsewhere, with reduced resources being devoted to sampling and analysis of crops
(for example, a sample of five to ten fields per crop across a project area). Resources could
then be devoted to a better understanding of erosion and manure application. Yield data (kg/ha
crop harvested) would of course be important for any efforts at modeling, but these are
considerably easier to measure in the field than full harvest, drying, and crop nutrient analyses.
4,000
4,000
4,000
Soil P
Soil N
Soil exch. K
3,000
3,000
3,000
2,000
2,000
2,000
1,000
1,000
1,000
0
0
0
Figure 10. Sensitivity of Soil N, P, and exchangeable K stocks to variation in nutrient
concentrations of these elements in harvested crops. The narrow confidence bands show that
there is virtually no sensitivity of the model to small errors or variation in crop nutrient and dry
matter content.
4. Sensitivity to errors in estimation of N loss mechanisms: We did not have precise measured
or modeled values for the two N loss mechanisms of denitrification and leaching2, since the few
models in use to estimate these in other parts of the world have been developed for systems
with inputs of soluble fertilizer N, which is not commonly used in Northern Potosi, or for
systems in the lowland tropics where nitrogen mineralization from organic matter is faster and
soil mineralogy makes leaching a qualitatively different process. We used values of
denitrification and leaching that led to losses equal to 50 kg·ha-1 N over the course of a six-year
rotation, concentrated at the beginning of the rotation after manure application for potatoes.
We then varied this rate by a factor of two in the sensitivity analysis. This analysis reveals that
N stocks are somewhat sensitive to the values of denitrification and leaching. The modeled
potato yield, however, was less sensitive than the soil N stocks to these loss parameters (Fig.
12). In comparison to other inputs with high sensitivity in the model for erosion and manure
2
Our model also ignores ammonia volatilization, since manure storage and application processes in Northern Potosí
likely lead to loss of most of the volatile ammonia N in manure, especially during the dry, windy planting season at
high elevation (low air pressure and quick volatilization before manure incorporation) in Northern Potosi. Our
model assumes that Ammonia N in manure never enters the soil budget, an assumption that also seems justified
seeing that it would likely contribute only about 10-20 kg·ha-1 per rotation even were there no losses.
16
application, it is probably not worth investing resources in precisely modeling or measuring
these N loss flows. This recommendation might change if increased rates of soluble N fertilizer
were to be used, which would likely drive higher rates of N loss from these flows. This speaks
of the importance of erosion, harvest, and manure management as the drivers of behavior of the
model, since they dwarf smaller flows such as these N loss mechanisms.
4,000
4,000
4,000
Soil N
Soil P
3,000
3,000
3,000
2,000
2,000
2,000
1,000
1,000
1,000
0
0
0
Soil exch. K
Figure 11. Sensitivity of soil nutrient stocks to uncertainties in rates of denitrification and
leaching. The higher variation in the soil N stock results from the fact that these loss mechanisms
directly affect only soil N.
20,000
15,000
10,000
5,000
0
Figure 12. Sensitivity of modeled potato yield to uncertainties in the rates of denitrification and
leaching in the model (range 0-100 kg·ha-1 per rotation). The low degree of variability in the
modeled yields may reflect that P rather than N is limiting yields near the end of the 50-year
timecourse.
5. Comparison of crop uptake dynamics to assess sensitivity to soil buffering of crop nutrient
uptake: Our model as originally formulated assumes that crops respond directly to the total
amount of the macronutrient present. This is potentially problematic, especially for soil P,
where it is likely that crops are in fact responding to a labile pool of P rather than its total
amount. To check for errors that the ‘total P’ assumption could make in the model, we
generated a test ‘buffered P’ scenario or thought experiment and compared model results
between the original model and the test scenario. The ‘buffered P’ scenario had an unavailable
‘occluded P’ pool that receives a percentage of any added P from manure (Fig. 13a). This is the
simplest version of what happens when there are multiple P pools of varying availability – the
result is to decrease the effectiveness of added P. We used the occluded P pool to ‘discount’
17
any P addition from manure by 30%. This 30% figure can be larger for applied fertilizer P, but
is likely justified for P applied in association with a carbon source as in the case of manure. We
also added an additional feedback mechanism that slows the occlusion of phosphorus as the
occluded P pool ‘fills up’ to an equilibrium level. The net effect was to discount P additions in
manure by about 25-30% over the course of the 50-year model run.
A second modeling attempt at representing labile versus unavailable pools was to
assume that instead of the ‘linear’ response of crops to declining fertility levels, there are two
types of ‘buffered’ response in which yields would respond differently because of ‘refilling’ of
available pools from less available but labile pools. These two types of buffered response
bracket the initial linear response of crop yield to fertility in the model (Fig. 13b), and form two
limiting cases for a sensitivity analysis of crop yield response in the model.
Figure 13a. A simple way to implement the presence of P occlusion, or sorption into unavailable soil pools. The
gate symbol leading to the
Occluded P pool represents a
Figure 13a
subtraction of 30% of manure
P into the occluded P pool with
Occluded P
Manure
each manure application.
P input
Figure 13b. The original
model assumes that crop
yields depend linearly on
nutrient levels in N, P, and K
pools (straight dashed lines).
The solid line represents
possible buffering behavior that
likely exists for nutrients such
as N and P. The dotted line
shows another possible
behavior, where buffered
behavior at low nutrient levels
follows a steep initial decline in
crop yield. The solid and
dotted line form the envelope
of possible yield behaviors at
low nutrient levels, and can be
used to conduct a sensitivity
analysis with respect to the
dynamics of nutrient stocks as
these are reduced due to
erosion and crop harvests.
3,000
Crop and residue harvest
Erosion
Figure 13b
Fully buffered
Relative
crop
yield
Linear decrease
Steep decline,
then buffered
Initial nutrient level
Nutrient level
Figure 14. effect on soil P stocks
of including an occluded P pool that
removes 30% of each manure
application.
kg/ha
2,000
Soil P
1,000
Without occluded P
With occluded P
0
18
3,000
15 a.
Soil N
kg/ha
2,000
Nutrient Buffering Mode
1,000
Steep decline, then buffered
Linear: reference mode
Fully buffered
0
15 b.
3,000
Soil P
kg/ha
2,000
1,000
Fully buffered
0
15 c.
3,000
Soil exchangeable K
2,000
kg/ha
Fully buffered
1,000
0
15 d.
20,000
Potato yield
15,000
kg·ha-1
potato 10,000
yield
5,000
Fully buffered
0
Figure 15 a-d. Effects of different buffering behaviors of soil nutrient stocks for crop uptake in Fig. 13b.
on the modeled time course of soil nutrient stocks and potato yield (12d)
19
Figure 14 shows the result of introducing an occluded P pool on total P levels, and
figures 15 a-d show the results for the envelope of buffering behaviors on the modeled nutrient
stocks and potato yield.
The effect of introducing a simplified occluded P pool is small enough to not warrant its
inclusion. In contrast, the sensitivity analysis on the way that crop yields respond to declines in
soil fertility shows some dramatic changes, particularly in potato yield, that argue for a more
sophisticated approach in this aspect of the model. Improving the model in this way would rely
on a more mechanistic approach, perhaps using longer-term experiments in fertilization or
observational data on a range of fields in different states of degradation (a chronosequence
approach). In addition, it might be useful, and a way to engage farmers more with the
usefulness of the model results, to gather farmers’ assessments of the way that yields have
declined over time. One relevant research question would be whether farmers notice a decline
in potato and other yields as fields become eroded, and whether this decline resembles the more
buffered, ‘optimistic’ upper curve in Fig. 15d or the dramatic 15-yr decline to a lowproductivity equilibrium under the ‘steep decline’ scenario.
Whether such improvements in the model are undertaken depends on the perceived
benefits from such an improvement. For now, it is enough to know that potential errors in the
model regarding feedback of crop yields to lowered fertility levels are likely dwarfed by the
importance of erosion as a determining factor how quickly agricultural fields are degraded
(Figs. 6 and 7), with manure (and other fertility inputs) being a close second for determining
cropping system sustainability (Figs. 8 and 9).
Using the model as a predictive tool:
Within the limitations outlined above using sensitivity analyses, it is possible to use the
model as a predictive tool for what the impact would be of future practices. Here we will
discuss three such scenarios: the impact of increased or decreased average manure application
rates, the use of green manures to enhance soil fertility and replace some or all of the manure
inputs, and the use of soil conservation strategies to reduce erosion rates.
Manuring rates: figures 16 and 17 show how modeled soil nutrient stocks respond to manuring
rates that bracket the average measured manure application in our study of 9 Mg/ha (Metric
tons/ha). Figure 16 shows the response for a soil slope of 12%, while figure 17 shows different
manure application rates on a slope of 5%. The most dramatic finding is the lack of impact on
total soil P of more than doubling the average manure application rate. The fact that even
substantially higher manuring rates (we measured very few ‘outlier’ farmers who applied over
20 Mg/ha manure) cannot keep pace with erosion of soil P is reflected in the disappointing
response of potato yields to increasing rates in figure 18a. Meanwhile, at the lower modeled
soil slope of 5%, soil P is adequately replaced by additional manure, and the decline in potato
yields is much less (Fig. 18b). The lesson that erosion must be reduced before increased
fertility inputs will have an effect on crop productivity is one that we designed into the farmer
game as a central lesson of the nutrient management game described below. During fieldwork,
a few farmer-leaders also made the same point that new initiatives with soil fertility such as
green manures would make little difference unless erosion was controlled.
20
Soil N
Soil P
Soil exch. K
Manure rate
19 Mg/ha
14 Mg/ha
9 Mg/ha
4 Mg/ha
0
years
50 0
years
50 0
years
50
Figure 16. Impact of varying soil manuring rates on N, P and exchangeable K stocks in a modeled farm field
with moderately steep 12% slope. Labeled order of curves at right is the same for all three graphs.
Manure rate
19 Mg/ha
14 Mg/ha
9 Mg/ha
4 Mg/ha
Soil N
0
years
Soil P
50 0
years
Soil exch. K
50 0
years
50
Figure 17. Impact of varying soil manuring rates on total N and P and exchangeable K stocks in a modeled
farm field with gentle 5% slope. Order of curves at right, from bottom to top, is the same for all three graphs.
)
20,000
15,000
g (
10,000
5,000
0
0
years
Fig. 18a
50 0
years
50
Fig. 18b
Figure 18a-b. Impact of different manuring rates on modeled fresh potato yield (kg/ha) for a 50-yr
timecourse with a. 12% soil slope and b. a 5% soil slope. The lines corresponding to different manuring
rates are so close as to not be easily distinguishable; they have been compressed in the model because P
is the most limiting nutrient for most of the application rates tested and also has a compressed behavior
dominated by erosion rather than the varied parameter of manuring rate (see middle P graph of Figs. 16 and
17 for this behavior).
21
Use of green manures to replace manure: one of the research interests we have pursued in
Northern Potosí is the intensification of legumes within crop rotations to provide the benefits of
additional nitrogen fixation and higher-quality crop residues that can raise the fertility of soils.
Use of green manures would also be useful to take grazing pressure off of degraded rangelands
that are supplying most of the fertility in animal manures. We can use the simulation model to
test a scenario of legume green manure use. A tarwi (Lupinus mutabilis) green manure before
potatoes in the rotation has been a strategy that World Neighbors has promoted in their work,
and we modeled the inclusion of a legume green manure every six years, in place of one of the
years of fallow that already existed in the modeled rotation.
As in the case of manure rate variation described above, we tested this scenario on a soil
with 12% slope and another with 5% slope. Farmers who adopt the green manure practices
usually still apply manures at a lower than usual rate at planting of potatoes, so we modeled two
scenarios of manure applications: one with 60% of manure applied, and one with only the green
manure and no animal manure applied. We used a figure of 6500 kg/ha green manure
incorporated into soil, which was an average of our experiments on farm fields in the project
area, for sites where tarwi was adapted and grew well. Fields with actual tarwi green manure
crops that we visited during the research ranged from 4000 kg/ha to 10,000 kg/ha upon visual
inspection, so we feel that this is a realistic estimate.
Figures 19 a-c show the model’s prediction of total nutrient stocks from these five
simulations (the 12% soil slope ‘control treatment’ of current practices, plus the combinations
of two soil slopes and 2 options for reducing manure inputs).
3,000
Soil N
Fig.
19a
5% slope, GM+ 60% of current manure
k g /h a
2,000
5% slope, GM+ no manure
12% slope, ‘current practices’
1,000
0
3,000
Soil P
Fig.
19b
k g /h a
2,000
5% slope, GM+no manure
1,000
12% slope, GM+60% of current manure
0
22
3,000
Fig.
19c
Soil exchangeable K
k g /h a
2,250
1,500
750
0
Figure 19 a-c. Results of simulations using different strategies of green manuring with Andean
lupine (GM). Graphs show comparisons of current practices on a 12% slope with two slopes (5%
and 12%) and two levels of manuring in addition to the green manuring: 60% of current, and no
additional manure.
The model predicts that green manure use has substantial positive impacts on soil N stocks,
especially where erosion rates are lower with a 5% soil slope. However, because green
manures add only N and not P and K, using them to replace all or even just 40% of the applied
animal manure leads to greater modeled declines in P and K levels than from erosion alone. In
making this conclusion we are not disputing that legume residues from green manures will have
the effect of making existing P and K in the soil more available by adding fixed carbon, as well
as improving soil structure and other soil properties. However these benefits may not be
enough to compensate for having discontinued the use of some or all animal manure, if rates of
erosion continue unchecked. Green manures need to be accompanied by reductions in soil
erosion, so that they have a beneficial impact on all three nutrients. Likewise, sources of P and
K such as manures will need to accompany green manures, even if at reduced rates.
Improved soil conservation measures: World Neighbors and other NGOs and municipal
government actors in the project area have also promoted the use of soil conservation measures
such as live and stone barriers in fields, or infiltration and soil-catchment trenches to capture
soil before it moves beyond the border of a field. Elsewhere, reduced tillage advocates and
traditional chaqmi or chaki-taqlla (Peru) and wachu rosado (Ecuador) practitioners have
managed soil with greater amounts of plant and residue cover, though these practices are not
known and have not been promoted in northern Potosí. In the final predictive simulation we
model the effect of soil conservation measures by modifying the parameters of the RUSLE soil
erosion models to values that reflect less steep, shorter-sloped, more residue-covered soil. In
addition, we also include in our analysis the case where a. wheat residues or b. all residues are
retained on the soil instead of being removed for feeding to animals, to see what impact this
might have over the medium to long term (Figs. 20 a-c). Figure 21 shows effects that soil
conservation measures and retention of crop residues currently fed to animals could have on
potato yields. Presumably, as farmers develop strategies of soil conservation, these benefits are
already being seen.
23
3,000
Fig.
20a
Soil N
2,000
g
Soil cons. + all residue retained
Physical soil conservation only
1,000
Current practices + wheat residue retained
Current practices
0
3,000
Fig.
20b
Soil P
2,000
g
1,000
Current practices
0
3,000
Soil exchangeable K
Fig.
20c
Soil conservation + all residue retained
2,000
1,000
Current practices
0
Figure 20 a-c. Effects of different scenarios of physical soil conservation measures and
retention of residues on fields after harvest. Physical soil conservation methods modeled are
the building of live and stone barriers to reduce slope, and partial retention of residues. “all
residue retained” means that tillage is still occurring during planting but residue is left as a
cover between planting. Dramatic impacts on cropping system sustainability appear possible
upon implementing measures to reduce erosion and conserve residues. For N and P, effect of
retaining only wheat residues on fields (i.e. after only the wheat year of the rotation) is small.
Soil K stocks are greatly benefited by cessation of all residue removal because much of the K for
crops other than potato is in the straw or stover harvested from the field to feed animals.
24
These final predictive simulations illustrate one of the central conclusions of our work with
nutrient budgets, which is that since erosion drives the behavior of the model, reducing it will
have major payoff to farmers in the form of more sustainable nutrient stocks in fields.
Retention of only wheat residues after harvest has only minor effects on soil nutrient stocks in
the model.
20,000
15,000
10,000
5,000
Soil conservation only
Current practices
0
Figure 21. Modeled effect on potato yields of implementing soil conservation and retaining crop
residues on fields after harvest. Reducing erosion has a dramatic impact, whereas retaining
residue on fields has little impact.
The three predictive simulations presented above are a final confirmation of soil erosion as a
downward driver of soil health, crop productivity, and in turn human health in highland Andean
cropping systems. The farmer game is our third outcome, and it embodies many of these
conclusions from the model as teaching points for farmers and development professionals.
Limitations and future directions for the model: in addition to informing the messages that
are included into the game and nutrient budgeting tool outputs of our project, we see the model
as a framework for testing and inclusion of existing soil fertility models in the Andean context.
Because erosion is such a dominant driver of soil sustainability, it is important to make
more precise the estimates of erosion included in the model, perhaps including a different soil
erosion model such as the Water Erosion Prediction Project or RUSLE, both of which would
have to be further calibrated and validated for the Andean region. It is also important to verify
experimentally that innovative practices among farmers such as reduced tillage, cover cropping,
or live barriers, is able to appreciably reduce erosion.
The way that soil nutrient stocks influence crop yield is another area where greater
precision could help the model in predicting medium term behavior. We attempted to do this in
an approximate way for phosphorus, but it ideal to attempt to model, or calibrate existing
models for internal cycling and different fractions of nutrient stocks such as P and N in soil.
The dynamic simulation model has provided important lessons that were immediately
applicable to the game and other outputs for farmer education, while raising interesting new
hypotheses and research questions for the future.
25
Outcome 3: teaching game for farmer nutrient management
To educate farmers and development professionals about the principle lessons gained from our
research in Northern Potosí, we developed a game for use in extension training sessions or
computer play. The game has a tabletop version that can be played with larger groups, and a
computer version that includes basic animation and a graphical interface. The principle
teaching objectives of the game came from our research in nutrient budgeting of farmers’ fields
in the project area and the modeling presented above:
1. Animal manure is the main soil fertility resource used to replenish nutrients that are
extracted by crop and lost to erosion from fields in Northern Potosí.
2. Nitrogen (N), phosphorus (P), and potassium (K) are the principle nutrients that manure
and other fertility inputs supply to crops, and crops export these nutrients in varying
amounts when they are harvested.
3. Although a larger herd size can generate more manure in the short run by harvesting a
greater amount of grazed vegetation and nutrients from rangeland, a strategy of larger
and larger animal herds to harvest more nutrients and replenish harvested nutrients and
soil losses in cropped areas is not sustainable.
4. As seen above in our modeling work, soil erosion will make or break the sustainability
of cropping systems. Unchecked erosion will lead to eventual failure of a field as a
productive resource, while managing it through soil conservation approaches allows
management with manures or green manures to build soil fertility.
5. Soil fertility underlies human health and family income.
6. Crop yields and soil fertility responds not just to management but to random weather
shocks, economic shocks, and health shocks that occur within the family.
7. Legumes are important as a source of health for soil, animals, and the household. When
legumes are grown under favorable conditions, they represent ‘free nitrogen’, and thus
protein, that enters the system
8. Under the current circumstances, aggressively controlling erosion, a moderate size
animal herd, and a diverse crop rotation including legumes is the cropping strategy most
conducive to sustainable soil fertility and income from the cropping portion of the
household economy.
As a result of these main messages, we aim to change attitudes and behavior of farmers in the
following ways:
1. Farmers will take more interest in soil conservation methods and seek to implement or
expand these as a key to cropping system sustainability.
2. Farmers will conceptualize fields as a ‘nutrient bank’ where exports must be balanced
by inputs.
3. Farmers will deepen their recognition of the link between maintaining a well-fed soil
and maintaining their own livelihoods.
Although some of the main messages and attitudes above are known by farmers, placing them
together within the context of a game centered on farm fields interacting with the household
during a crop rotation allows these lessons to be more concrete. It also allows farmers to
potentially disagree or enter into a dialogue with the main messages or ‘rule set’ of the game,
which can be useful in generating feedback to researchers and extension staff and putting
additional questions to modeling research. In regards to erosion, farmers are often surprised
when shown the amount of soil that their fields lose every year in mountainous areas like the
Andes (personal communication, Amelia Henry) – so that quantifying this effect as the ‘silent,
26
stolen crop’ that leaves a farmer’s field every year is indeed helpful to changing farmer
attitudes toward soil conservation work.
The structure of the game: the game involves a series of 5 to 15 rounds depending on the time
available and the format (faster play is possible in the computer version). Each round
represents one year of cropping. The player has assets that model the assets of a typical farm
household in Northern Potosí:
•
•
•
•
Land: 3 or 6 fields for the tabletop and computer versions respectively. The fields are
modeled as 0.1 ha in size, a typical size of a field in the project area, so that the numbers
of beans or the figures presented in the computer game can be immediately visualized as
quantities (kg) of nutrient entering or leaving a typical field. Each field has a stock of
N, P, and K in kg., and also an annual erosion rate (low, medium, high) that can be
lowered by investment of money and animals for soil conservation infrastructure. In the
tabletop version, each kg of nutrient is represented by one bean. The magnitudes of the
nutrient flows are those that we measured in the research.
Money: Currency can be used to buy animals, improve land, or paid out to help when
health or economic shocks threaten.
Animals: directly related to the amount of manure that is available to apply to the fields
in each round. Investment in animals to generate more manure is a situation of
diminishing returns, because overgrazing occurs.
Health: measured in ‘health units’; increasing the stock of health units is a main
objective of the game, and can be done through growing legumes and having high yields
of other crops. Health is lost through poor-yielding crops and also when health and
economic shocks occur.
Structure of a round of play: before describing the table top version and animated computer
version of the game, we diagram the cycle of a round of play common to both versions:
1. Choose
crops for each
field within a
rotation plan
7. Return to start
for next round
6. Make investments
in land or animals to
improve functioning of
nutrient management
Weather shock
impacts on crop
yields
2. Calculate and
allocate manure to
fields
3. Calculate crop
yields and crop
export of nutrients
5. Suffer/Respond to
economic shocks
and add/ subtract
from assets based
on crop yields.
4. Subtract erosion
losses of nutrients
Economic and
health shocks
Figure 22. Structure of a game round, common to the table-top and computer versions
27
Two versions of the game were developed during the project:
The tabletop or farmer workshop game: (details and game materials in the appendix and on
the short video of the game): this is necessarily simpler than the animated computer version,
since many of the manipulations must be made by the players themselves, for example the
moving of beans onto and off of fields to represent nutrient flows. This physical involvement
of players in carrying out the mechanics of the game is in fact a strong point for many
audiences such as farmers or college students, because it allows learners to observe in a
tangible way the meaning and magnitude of nutrient flows like erosion or manure application.
The table top game is played on only three fields (compared to six in the video game), which
start at high, medium, or low fertility levels, and with three different levels of erosion (see
appendix 3 for the game materials). If two teams (families) are formed, then the initial resource
allocation (money, field, animal assets) can be altered to illustrate how wealth differences
change the game for two different households. Most commonly we have played with a
‘wealthy’ (22 animals and 20 money units) household team, and a ‘poor’ (14 animals and 12
money units) team, to illustrate how it is easier for wealthier farmers to invest in the
productivity of their soil and survive weather and economic shocks.
It is essential to include an evaluation and sharing period at the end of playing the game,
in order to score the two teams on how their fields, herds, and health and money assets
performed, or evaluate to what extent each team used legumes in its strategy. This is also a
chance for players and facilitators to voice their take-home messages from the game, question
aspects of the rule set that do not correspond with their experience in farming, or recommend
new features. Three interesting examples of this process were gleaned from playing the game
with farmers and NGO technical staff, undergraduate Cornell students, and a mixed group of
graduate students and researchers (portrayed in the video).
Figure 23: Farmers and World Neighbors staff engage in a trial of the farmer nutrient
management game in Kisivillque, Northern Potosí, Bolivia, in July 2008. Each plate represents a
field, with three compartments for N, P, and K represented by different color beans.
28
The group of farmers and technical staff in Bolivia appreciated the teaching objectives
about soil erosion, the importance of animals, and the way that nutrients flow into and out of
soils. They also made the subtle observation that in spite of a greater ability to invest in both
animals and soil conservation, the wealthier group of farmers took a cavalier view of soil
erosion because of their ability to gather large amounts of manure and nutrients with the large
herd of animals that they purchased. They then confirmed that this attitude has its real-world
version among the wealthiest farmers in each community: they reported that the poorer and
medium-resource farmers take the most interest in conserving their soil resources. The
Bolivian test group also suggested that more crop options should be available for planting,
because they wanted to learn about how these crops remove nutrients from the soil, and also
recommended that the buy and sell price for animals allow for monetary gain, which is exactly
what happens in their households: raising a young animal to maturity requires care and
resources, and can be a source of income. We have incorporated the latter suggestion into the
game, and will also work to incorporate the former.
The group of Cornell students found that learning about the intricacies of how
smallholders in the Andes manage crops and soil nutrients was very interesting. They enjoyed
the fact that it required physical manipulation of beans on a board, but felt that the computer
version would be helpful for allowing larger groups to play individually at one or two players
per computer, and then compare their games. After a discussion of the social context of
farmers in Bolivia, they also suggested that another investment strategy to include in the game
would be to invest in the education of children, which would require a number of rounds of
payments and perhaps some work or health consequences. This investment, which is in fact a
phenomenon in most of the developing world, could pay off later in the game in the form of
higher income sent from a city or other place of employment, which then allows greater
investment in the farm enterprise.
The research group had another set of insights about the game, some of which are
presented in the short video submitted with this report. They wanted a better accounting of the
nitrogen credits to soil from the root biomass of legume crops, and suggested ways to make the
benefits and tradeoffs of different crop choices more interesting. They felt that there was a
complexity to mastering the intricacies of each round of play (see appendix for game
instructions), but once these were mastered they could relax and think about their strategy and
the tradeoffs they had to confront as players. They also felt that health assets should be more
closely coupled to other events in the game. After observing his team plant only potatoes and
maize for two subsequent years, a nutrition graduate student felt that there should be a greater
health penalty for lack of diversity in the diet.
Next steps for the table-top game: World Neighbors Bolivia has indicated an interest in
improving the table-top game and bringing it to market in the form of a game set and materials
that could be used with farmer groups and provided to other organizations working with
farmers. The game materials used in the short video included with these report materials are a
prototype for such a game set.
29
Figure 24: The main screen of the animated computer game.
Animated computer version: the computer version of the farmer game is an ambitious attempt
to bring the concepts of nutrient balances and nutrient management into an appealing, fun game
format that could eventually run as a web or cell phone application or as a standalone installed
game on an offline computer. Because of the large amounts of work required in game
development with a video interface, it is the portion of the grant deliverables where the most
remains to be done in perfecting the game product. Nevertheless we feel we have produced an
interesting and fun proof-of-concept for motivated players who already know something about
farming and nutrient management, such as Andean farmers or school and university students
with the assistance of a facilitator. The game also features some initial examples of delivering
content about nutrient management as side tutorials to the main game: a short animation that
illustrates how nutrients enter the soil through the application of manure, and several brief webpage format tutorials about the function of legumes and the roles of N, P, and K in crops. In the
future this type of embedded tutorial could be expanded to direct links to other educational sites
for smallholder farmers. An excellent feature of these tutorials is that they are editable webpages that are easily modified and then re-inserted into the game with an updater program that
is distributed with the game. We are grateful to our programming intern, Bhuvan Singla, for all
his hard work on the project
The game is currently an offline package of about 20Mb size with both English and
Spanish versions, intended as downloadable installation files for distribution on the McKnight
website. Our programming intern has additionally generated structures appropriate for online
play and scorekeeping (see the ‘third world farmer’ online game, cited below for an example of
such scorekeeping and social web functioning of the game), which we placed on a test site
during the game’s development. Currently, the McKnight website host at Cornell University’s
Mann Library is not able to host this structure, which is why we have fallen back to a simple
installable and offline version of the game. Figure 25 shows the overall high-level design of the
game, with allowances for adapting it to different regions of the world where nutrient
30
management challenges may be different as well as to online and mobile phone platforms. The
game is written in the asp.net framework and programming language which relies on the
Microsoft .net environment in a Windows XP platform. This means that if not already installed
on the computer, the dot.net environment must be downloaded and installed (free from
Microsoft).
Figure 25: Proposed structuring of the game from programmer Bhuvan Singla with internet
functionality and multiple platforms (online, offline, mobile phone play), as well as ability to
restructure language and other parameters of the game to reflect farming systems in different
regions.
Comparison with other farming simulation games: our nutrient management game is
superficially similar to other simulation games involving farming, such as SimFarm
(http://en.wikipedia.org/wiki/SimFarm) , TheFarming game (a board game), The Farmer
(http://www.dailyfreegames.com/flash/arcade-games/the-farmer.html) and Third World Farmer
(playable at http://www.heavygames.com/3rdworldfarmer/gameframe.asp). All of these
emphasize the farmer playing against the risks and costs associated with crop production in an
attempt to better their livelihood: either breaking out of poverty or becoming as wealthy as
possible. Third World Farmer seeks to raise consciousness about the difficult situation faced
by smallholder farmers. The objective of our game is somewhat different: we focus on
livelihood, but especially on its relationship to levels of soil nutrients and the challenges of
conserving soil. We have particular messages from research and modeling that we want the
game to impart, and we see the game as a venue for farmers and other learners to access content
about soil and crop management in the fun, non-threatening environment of a game. Because
of the underlying nutrient budgeting methodology, we have made soil nutrient stocks a key
31
mechanism of feedback to the player on her success in the game. We feel that the dynamic
nature of the nutrient stocks and the visualization of these, related to health, animals, and other
livelihood assets, is an innovative and very promising feature of our computer version. In
addition, we have designed the game with Spanish and English versions, which is unique so far
as we know for a farming game.
Brief review of video game architecture: while developing the game, our programming intern,
Bhuvan Singla, undertook a review of design principles in video role-playing games, and
assembled the following list of common principles:
• Ease of accessing basic, satisfying features of play in the first playing.
• A surplus of features so that subsequent games lead to the discovery of additional
interesting features.
• Ability to improve play through learning the best strategy for subsequent games.
• Increasing difficulty within the progress of one game that allows for differentiation
between successful and poor strategies.
• Difficulty level set to provide encouragement as well as motivation to achieve good
outcomes of increased wealth or breaking even.
• Chance events, sound, and animation add to the fun setting of the game.
In addition to these general game principles, we had additional objectives related to the context
of farmer education:
• Realistic features allow farmers to learn about their own system. For example, the
quantities used to track nutrient levels in fields are calibrated to represent kilograms of
nutrient entering or leaving a typical field size (0.1 ha or 1000 m2) for Northern Potosí.
• The rule set involved ‘artificially’ strong or simple factors that are intended to drive
home the main messages (for example, erosion is an more impressive and obvious a
threat than it often seems to farmers, more animals mean more manure until
overgrazing occurs).
• The game is directly linked to educational content called tutorials. In one case we have
authored a short animated sequence to teach about the nutrients that manure contains,
along with carbon that feeds soil life (Fig. 26 and 27, below).
Figure 26: Screenshot of the manure nutrients animated tutorial.
32
Figure 27: Screenshot of the nitrogen webpage tutorial, showing the appearance of nitrogen
deficiency and sufficiency in crops.
The computer version of the nutrient management game follows the same steps as diagrammed
for the tabletop game in figure 22. However, the computer handles record-keeping for fields,
and the algorithms governing crop yield and nutrient applications more efficiently so that a
more complex game can be designed. The computer version thus has six fields instead of three
for the tabletop game, and the tactile training of players moving beans on and off of fields is
replaced by animations that represent the adding and subtracting of nutrients with flows like
manure application or harvest.
The computer version also gives a player more constant feedback on how successfully
they are building soil nutrients and livelihood, in the form of a graph of their soil nutrients over
time and a scoring matrix that is displayed after every round. In the scoring matrix, players are
evaluated on their total nutrient stocks in fields, their health level, and also how efficiently they
are directing the soil and crop nutrients to the benefit of their household in harvests, compared
to the amount they lose each year to soil erosion.
33
Figure 28: screenshot of the round by round scorecard, showing a game where the player
invested maximally in controlling erosion so that the efficiency of their manure applications went
up (NPK efficiency) and also grew legumes to improve their health status.
The graph of nutrient tendencies in the soil for the three macronutrients N, P, and K is a
principal output that helps the player to assess whether they were able to control erosion and
build soil nutrients over time and thus become a successful farmer. Possessing sufficient
animals is also important to this goal.
Successful game
Worst-case game
N
K
K
N
P
P
Figure 29: Nutrient graphs from the log for a successful game at left, where a player was able to
build field nutrient stocks by managing erosion and maintaining animals, and a ‘worst-case’
game at right, where nothing was done to control erosion.
The computer game contains the same opportunities to invest as the tabletop game: players can
install soil conservation measures on their fields, buy animals to generate more manure, or sell
animals to make money for any of their needs. Because of the greater functionality of the
computer environment, other opportunities for investment appear during the game, such as the
establishment of an irrigation system that raises yields in drought years and a savings scheme
where deposited money can be withdrawn with interest at a later time (a credit scheme or
insurance scheme for crop failure would be another interesting addition to the game).
34
The game also becomes harder as successive years pass, through the introduction of
stress events such as low pasture years when animals die, or massive flooding that reverses the
positive effects of past investment in reducing soil erosion, so that more resources must then be
invested to combat soil erosion. These stress events are designed to make the game more
interesting. They also require a player who has successfully invested in their land and is
beginning to raise their economic level to practice their strategy for success one more time, or
alter it slightly in an attempt to respond to the stress event.
A player can also choose to send one or more children to pursue a high school
education, which requires resources that may compete with tackling erosion and other
challenges but can also bring additional resources in the form of scholarships.
Future steps on the computer game: due to the short time and small group involved in
developing the game, we were not able make the game fully transparent and accessible for a
non-literate audience. For example, we use time-based trend graphs to deliver evaluations of a
player’s success in building soil nutrient levels, and a numerical rating scale to describe the
levels of nutrients in the different fields. It is likely that neither of these would be familiar to
players with less scientific or numerical literacy who might play the game. As we began to
recognize the large amount of work involved in developing a fully-functional animated game,
our primary objective became developing the set of underlying game mechanics (algorithms,
conditions, governing rules and events) that felt satisfying and interesting, as well as delivering
content about soil nutrient management. We feel that a list of priority upgrades to the game
would make it even more successful at delivering messages to audiences of varied life
experience and educational level:
• Show the movement of actual units of nutrients in and out of fields by using color-coded
dots or symbols to represent the three nutrients, analogous to beans in the table-top
game. Track all the nutrient flows in a way that would be recognizable and entertaining
to smallholder farmers.
• In conjunction with field-testing, be sure that all transitions between game steps and
transactions such as harvest, manure application, erosion, and weather risk, are well
understood by players. We suspect that our own bias as developers blinds us to
instances in the computer game where players may not see why a particular nutrient
flow, event, or investment is important or relevant to their progress in the game or the
real-life situation.
• As is already the case for the table-top game, provide for the use of legume green
manures to enrich the soil with nitrogen and make phosphorus more available.
• Improve the existing tutorial content on nutrient management for crops, and expand the
tutorials to include information on erosion, livestock, and pest management.
• Introduce an additional feature by allowing the farmer to purchase, borrow, or rent one
additional field – this presents additional challenges but allows the harvest of more
nutrients.
Outcome 4: policy brief for local and regional government actors
We will be writing the policy brief in Spanish over the next few weeks, and expect to
abstract much of it from this project report, which is the most complete reporting of our project
activities and outcomes. Steven Vanek will be in Bolivia between January 8th and 20th of 2009,
to share this outcome with World Neighbors for its inclusion with other research materials to be
shared with local government entities. We will also submit this brief to the McKnight
foundation for inclusion with our other report materials and outcomes. We are very excited by
the prospect of World Neighbors Bolivia now being linked to the Plataforma nacional de
35
suelos or Bolivian national soils network, which may play an influential role in new Bolivian
legislation regarding soils to be authored under the current Bolivian administration.
Highlighting the importance of soil erosion and soil nutrient management for marginalized
smallholders in highland Bolivia is an important role that we hope to support by sharing our
model outcomes and educational products. Briefly here, we would hope to promote the
following outcomes at the level of local government policy:
1. Demonstrating using the simulation model presented above that crop rotations are likely
close to balanced with respect to soil nutrients, in the absence of erosion. This balance
is likely also dependent on manuring rates, which may depend in turn on socioeconomic
status. However, preliminary work to analyze this aspect (not presented here) shows
surprisingly little variation in manuring rates driven by economic levels of farmers.
2. Permanent commitment to supporting smallholder farmers in work to reduce soil
erosion, in the form of credit, support programs, making available plant and seed
materials for live barriers. etc.
3. Consolidation of existing results from past research on soil erosion, and use of these to
craft better responses to this problem. A number of promising practices such as live
barriers with naturalized grasses are known to be in progress and would be in need of
expansion.
4. If there are to be programs that support the use of fertilizer, ensuring that these are
available in some ways to smallholder farmers, as a way to diversify their nutrient
inputs rather than as a wholesale switch from manure to fertilizers. It would seem that
in this regard and given past massive nutrient losses from soil erosion, phosphorus and
potassium would be key nutrients to support increased productivity and the use of
legumes.
5. Awareness that legumes, especially green manures, are an important way to improve the
nitrogen balances of farming systems, although they likely not solve the issue of
phosphorus or potassium loss through erosion, which must be addressed head-on as the
most urgent priority.
Outcome 5: materials on crops and soils for the Andean Community of Practice
Our project objectives included being a resource on soil science for the Andean
Community of Practice, with contributions to the listserv and participation in the CoP meetings.
As we began to prepare content for the animated computer game, we also prepared resource
materials on crop macronutrients (N,P,K) in the Andean region, which was sent to the listserv
and also posted on the McKnight website at our project site. The materials have also been
translated into English. World Neighbors in Ecuador and Bolivia have both used these
materials in training technical staff with their participating projects. We have included these
materials as an appendix to this final report.
We also wish to return our original data from sampling of harvests to farmers in a
useable form. We have prepared materials with a bar graph format that illustrate how many
kilograms of different nutrients are extracted from the soil with typical crops in the research
area at different typical yield levels. These graphs are also included as supporting materials in
the appendices. The bar graph format was suggested to us by a group of farmers in Sak’ani,
Northern Potosí, as the one they most readily understood, and the units of yield are given as the
ratio of units of yield to units of seed, which is the dominant measure of yield understood by
farmers in the area. These materials were shared with communities in July of 2008.
36
References:
Karpinets, TV. Greenwood, DJ, Ammons, JT. 2004. ‘Predictive mechanistic model of soil
phosphorus dynamics with readily available inputs.’ Soil Science Society of America
Journal 68(2): 644-653.
Leon, CC. 2005. A multi-scale approach for erosion assessment in the Andes. Wageningen:
Wageningen University and research centre.
Montgomery, DR. 2007. ‘Soil erosion and agricultural sustainability’. PNAS 104 (33): 13268–
13272.
Renard, KG, Laflen, JM, Foster GR, and DK McCool. 1994. “The Revised Universal Soil Loss
Equation.” Ch. 5 in Lal, R. Soil Erosion Research Methods. SWCS/St Lucie Press:
Delray Beach, Florida.
37
Appendices
1.
2.
3.
4.
5.
Nutrient Budgeter tool guide
Model diagram from Vensim
Materials for table top game
Help file from the computer game
Nutrient materials from website (Spanish)
6. Graphs provided to farmers participating
in nutrient sampling of crops (Spanish)
Additional materials on DVD:
1. Video of test game at Cornell, 12/08
2. Flash video teaching animation of
nutrients in manure
3. Vensim model file
38
Appendix 1: Help file for the Nutrient Budgeter tool program:
Nutrient Budgeting Tool v1.0
•
•
•
•
•
•
Contents:
Introduction
Units of measurement
How to enter a year of data
Seeing the patterns
Saving data
Adding your own composition data
Introduction
This Nutrient Budgeting Tool allows a user with farm management records whether levels
of nitrogen (N), phosphorus (P), or potassium (K) in a farm field are being built up or
depleted. Management records can be exact, approximate, or imaginary in order learn
about the effects of different management options. The program keeps track of a farm field
as a bank account, where nutrients are put in and taken out, just as in a real farm field.
Nutrients are put in as fertilizers, amendments, or green manures, and taken out through
harvest or removal of residues, or as losses through erosion, leaching, and denitrification.
Figure one displays these additions and losses. Just as for a bank account, the balance is
a calculation of additions minus losses, on a yearly timestep. The software is intended to
make calculating and visualizing these budgets as easy as possible, with the use of dropdown menus, simple graphs, and a list of the most common amendments and crops
managed by farmers.
Figure 1. How the nutrient budgeter sees a farm field:
Flows that increase
nutrient stocks
Manure/Compost
Chemical fertilizer
Nitrogen fixation by
legumes
Flows that decrease
nutrient stocks
Crop harvest
Farm
Field
Removed crop
residues
N and K leaching
Denitrification
Soil erosion
The budgeter is somewhat approximate (plus/minus 15 lbs/ac. or kg/ha, roughly), since it
is hard to estimate exact values for flows of nutrients like erosion or nitrogen fixation by
legumes. However, much can be learned from these approximate nutrient balances,
since crop rotations are often either running large surpluses (For example 50 lbs/ac.
phosphorus (P) surplus per year is ‘large’ and will accumulate in soil) or substantial
deficits that deplete the nutrient stores in soils. The starting state of a soil needs to be
taken into account, using measures like crop productivity or soil tests that farmers and
extensionists already know and use. A soil exhausted of nutrients may require nutrients
to be applied in surplus over the short term. Fields with adequate nutrient levels should
be maintained in rough balance, and rotations that produce large excesses of polluting
nutrients like N or P need to be modified to avoid impacts on the local environment.
39
The nutrient budgeter can be used to analyze different crop management scenarios, for
example the impacts of different manures or application rates on nutrient stocks, or the
difference in nutrient export between different crops in a rotation. It can also be used to
see which of the major nutrients are at risk of having surpluses or deficits, given an actual
rotation of crops and applied nutrients, or a simulated rotation that the user inputs for
learning purposes.
Units of measurement
For correct results, users need to understand the units used for entering amendment,
green manure, or crop harvest data. Except for grain crops, the program uses either
English units of lbs per acre, or the metric units of kg per hectare. A button on the top
row of the main screen allows switching between these units. In the explanation below
lbs/acre is assumed as used by most U.S. farmers. The corresponding units of kg/ha
used in other countries is stated in parentheses.
Amendments:
• All amendments should be entered in lbs/acre (kg/ha for metric users).
• Moist weights should be used for manures or composts, so that additional
conversions to dry weights are not necessary. This means that standard U.S.
tons/acre should be multiplied by 2000 to get lbs/acre, or metric tons/ha by 1000 to
get kg/ha.
• Synthetic fertilizers should be entered as their applied weight per acre, for example,
‘300 lbs/acre diammonium phosphate’
Green manures
• Green manures should be entered as lbs/acre dry matter. If direct sampling and
drying was not done to determine this, it may be necessary to estimate the green
manure dry matter. Green manures usually range in dry matter per acre from 1000
lbs/acre for a fair to poor stand, up to 10000 lbs/acre for an excellent-performing
green manure. Extension personnel and farmer networks may be able to assist
with these estimates. For example, most farmers growing hay have a good feeling
for how many lbs/acre of plant biomass is present with legume crops that look poor,
fair, or good.
• For green manure crops that combine a legume with a non-legume, it is important
to enter only the legume portion of the dry matter.
• Legumes are represented as adding only N to the field, since P and K are recycled
by these crops and only N is added to soil stocks.
Crops
• All crop harvests are entered using lbs/acre (kg/ha), with the exception of grain field
crops where a standard bushel system (bushels/acre) is used.
• Vegetable farmers who measure yield in cwt/acre (hundredweight) will need to
multiply yields by 100 to get lbs/acre.
• Field corn, soybeans, oats, barley, wheat, and other grain crops are entered using
bushels/acre. However, grain straw is entered as lbs/acre (kg/ha)
• Farmers using metric kg/ha units of yield for grain crops will need to convert their
yields to bushels per acre, even when the ‘kg/ha’ units button is selected. The
following table gives the factors to divide to make this conversion:
40
How to enter a year of a crop rotation into the budgeter
1. Assemble real or imagined information for the management of your farm
corresponding to one field. You’ll need all the fertility amendments used,
measurements or estimates of biomass for any legume green manures that were
plowed in, and the harvest records or estimates, including any straw or other
residues harvested.
2. Figure 2 below explains the main screen of the budgeter and areas for entering
information for each year of a crop rotation on soil amendments (compost, manure,
fertilizer), green manures (legumes grown for soil fertility), crop harvests, and
losses.
1. amendments
2. green manures
3. crop harvests
4. losses- radio buttons
Detailed
accounting of
inputs and
outputs
Yearly summary and
corresponding
graph point
Figure 2: guide to the main screen of the nutrient budgeting tool.
41
3. Amendments: In the amendments box, use the pull-down menu as shown below
(example- poultry manure) to select from the amendments listed, and enter the rate
of application in lbs/acre (or kg/ha) to the right of each amendment. Use fresh
(moist) weights per acre (hectare) for composts and manures. You can enter up to
three amendment entries (A1-A3) in a single year. Use integer values, no
decimals.
Entering a poultry manure application
4. Green manures: Enter the dry matter biomass of any legume green manures
plowed in prior or during the growth of the crop. Select a legume from the dropdown list as for amendments, or one of the generic entries reflecting the percent N
of the legume plowed in. Entering the dry biomass in lbs/ac (kg/ha) may require
estimation if no direct measurements of legume biomass was made. Enter only the
legume portion of the dry matter for a mixed legume/nonlegume green manure.
You can enter up to two green manure entries in a single year.
42
5. Crop harvests: Enter the yield of a selected crop. Select a crop from the pulldown list, and enter the yield in lbs/acre (kg/ha) fresh weight, except for grains
where the yield must be entered as bushels per acre. Grain straw that is removed
from the field should be entered as a separate entry using lbs/acre (kg/ha). You
can enter up to three harvested crop entries in a single year. Remember that some
vegetable crops have two parts harvested, i.e. carrots or beets that are harvested
with their tops, when these are removed from the field.
Entering a barley grain yield of 55 bu./acre
6. Losses: the nutrient budgeting tool can make rough estimates of losses of N, P,
and K due to erosion, leaching, and denitrification. These are selected using a
radio button for zero, low, medium, and high (see Fig. 2, circled section 4). It may
be useful to first omit losses (leave the buttons set to zero) and see if a rotation is
tending to accumulate large amounts of N or K, in which case leaching of N and K,
and denitrification of N, can be substantial. Erosion will depend on slope, soil type,
and measures such as cover cropping or physical modifications taken to prevent it.
We have used values for these loss flows that are intended as an illustration of how
much can be lost from farm fields, and a teaching tool for the importance of
preventing erosion and not applying soluble nitrogen at times of year when
denitrification losses can be significant. Although based on a review of measured
flows in actual farming systems, these loss nutrient flows are certainly the least
precise part of the tool.
7. Finished! Click on the ‘calculate’ button, in the top menu bar:
This will set input your data for year one, and you should see the graph change to
reflect the net balance of nutrients from in the first year. (see example below)
43
Seeing the patterns:
Successive years are entered in the same way as this first year. Advance to the
second year by choosing ‘year 2’ from the pull down list in the top menu bar.
Graphs: The graph of increasing, stable or decreasing nutrient status is
updated in the graph area of the screen each time you click on ‘calculate’.
You can see N, P, and K together (all-in-one), or a graph for each of the
nutrients, by clicking on tabs at the top of the graph area.
Results table: The results table at the bottom of the screen shows the total
balances of N, P and K over ten years. If you didn’t enter data for a year, it will
show zero. Single click on a cell to see details in that year on right side.
Detailed accounting area: the text area at right of the screen shows a
detailed accounting of inputs and outputs for a single year of the budget (see
44
Fig. 2). To select which year shows in the budget, click on the cell in the
results table at the bottom of the screen, which will then be highlighted in
yellow indicating which year is currently shown in the detailed accounting
area.
Saving data from the budgets:
The nutrient budgeter tool allows you to save the data from your budgets as an html
and excel file. Use ‘save’ (floppy disk icon) from the top menu bar to access the save
dialog box (see below). Graphs are not saved with the values, but a graph can be
created later in excel using the saved data. A graph can be saved without
accompanying data, in .png format by right-clicking on the graph area in the budgeter
main screen and choosing ‘save as’. This format is read by many image programs. In
this version of the budgeter it is not possible to re-open saved nutrient balance data in a
following session of the program. However, enough detail is given in the saved excel
and html files so that data can be re-entered for illustration purposes.
Adding your own N, P, K composition data for soil inputs and crops:
You can also add new items to the amendments, green manures, and crops lists by
clicking on the green plus or “add new items” button:
A small window will pop up. Enter the names and values in %N, P, or K, and then click
“OK”. For example you would enter ‘my_compost’ with 1.2%N, 0.5%P, and 1.7%K as
‘1.2, 0.5, 1.7’ in the corresponding boxes (see below). The new items will appear at the
bottom of each item list. The %NPK values you enter should reflect the way the
different items are entered in the budgeter: as a % of moist weight for amendments, %
of dry wt. for green manures, and % of fresh or harvested weight for crops. Where
analyses have been reported as % of dry matter, this may require correcting for
moisture content.
45
Quick reference for buttons in menu bar, from left to right:
Exit – Exits the program
Save—Saves the current numerical results for the ten-year budget as excel and
html files
Help – Opens this help file
About – gives version, contact, and author information
Change units – changes units between lbs/ac and kg/ha. Note that in kg/ha
version, grain crops must still be entered as bushels/ac.
Add new items – allows the user to enter new, local information on NPK
composition of amendments, green manures, and crops
Choose a year – drop down list for choosing a year to enter, review, or change data
Calculate – sets new changes since the last ‘calculate’ operation
Undo – undoes the last change
Tips on hovering: When the mouse cursor hovers on a button or box, the program will
show you a tip about its function, like this:
46
Appendix 2: diagram of the Vensim dynamic simulation model for the NPK balance of a field developed during the project.
reference wheat yield
number of years
fallow
reference maize yield
fallow vegetation
biomass
random impact on
wheat yield
random impact on
fava bean yield
amt fixed N in
fallow biomass
reference fava
bean yield
fallow vegetation
%fixed N
Fava Bean
yield
kg/ha
manure N content
manure N application
Proportional
Yield Shock
random impact on
rainfall erosivity
<zero dummy switch>
potato DM N content
field 1 - N
lookup for potato
response to N
<potato yield kg/ha>
<Initial Level of N
in Field>
response to N
<Wheat yield kg/ha>
N leaching
reference
denitrification rate
Potato yield
f(Ncurrent)
<pulsetrain
combined>
Maize yield
f(Ncurrent)
fava bean yield
f(Ncurrent)
Lookup for Wheat
Response to N
Wheat yield
f(Ncurrent)
Ratio of Current
to Initial N
fraction sorbed of
manure P
switch for high P
sorption
initial value of occluded P
occluded P
<zero dummy switch>
fava bean yield
f(Von liebig)
<Fava Bean yield
kg/ha>
Maize
DM P
content
<wheat yield
f(Von liebig)>
response to P
lookup for wheat
response to P
<Wheat yield
kg/ha>
fava yield
f(Kcurrent)
wheat yield
f(Kcurrent)
lookup for maize
response to K
response to K
lookup for wheat
response to K
<maize yield
f(Von liebig)>
Wheat yield residue
adjustment - P
Maize yield residue
adjustment - P
Fava Bean yield
residue adjustment - P
<fava bean yield
f(Von liebig)>
<residue removal
switch - Wheat only>
e
t
a
r
n
o
i
t
a
c
i
l
p
p
a
Wheat yield
f(Pcurrent)
lookup for maize
response to P
<Fava Bean
yield kg/ha>
maize yield
f(Kcurrent)
lookup for potato
response to K
e
r
u
n
a
m
;
s
n
o
i
t
c
n
u
f
p
u
k
o
o
l
e
s
n
o
p
s
e
r
<Wheat yield kg/ha>
Fava bean yield
f(Pcurrent)
lookup for potato
response to P
<potato DM
content>
P in maize harvest
<Maize yield
kg/ha>
Potato yield
f(Kcurrent)
p
o
r
c
d
n
a
n
o
i
s
o
r
e
l
i
o
s
f
o
s
e
p
a
h
s
;
s
r
a
e
y
<residue removal
switch>
wheat yield f(Von
liebig)
<Maize yield kg/ha>
Potato yield
f(Pcurrent)
Maize yield
f(Pcurrent)
potato DM P content
<potato DM
content>
reference soil
exchangeable K
content
w
o
l
l
a
f
;
e
s
u
e
r
u
n
a
m
n
e
e
r
g
;
s
e
h
c
t
i
w
s
<wheat yield
f(Kcurrent)>
<potato yield kg/ha>
Fava
Bean
DM P
content
potato DM K content
l
a
v
o
m
e
r
e
u
d
i
s
e
r
;
E
L
S
U
R
n
i
s
r
o
t
c
a
f
maize yield f(Von
liebig)
<fava yield
f(Kcurrent)>
P in wheat
harvest
P in fava bean
harvest
potato
FW K
content
ratio of current to
initial K
P
,
C
,
S
L
:
n
u
r
h
c
a
e
n
o
k
c
e
h
c
o
t
s
g
n
i
t
t
e
s
<maize yield
f(Kcurrent)>
Wheat
DM P
content
fava yield residue
adjustment - K
<fava bean yield
f(Von liebig)>
<potato yield
f(Von liebig)>
initial level of
exchangeable K
in field
<potato yield
kg/ha>
Potato
FW P
content
Erosion P flow
ratio of current to
initial P
potato yield
f(Von liebig)
<potato yield
f(Von liebig)>
<pulsetrain
combined>
P in potato harv est
initial level of P
in field
<Potato yield
f(Kcurrent)>
K in fava bean harvest
K in potato harvest
Erosion K
flow
<manure K
application>
equilibrium value of
occluded P
field 1 - P
<pulsetrain
combined>
Fava bean
DM K
content
lookup for erosion
rate of K
manure
application - P
<maize yield
f(Von
liebig)>
Maize DM
K content
field 1 exchangeable K
Manure application - K
<manure P
application>
maize yield residue
adjustment-K
K in maize harvest
<manure K content>
lookup for erosion
rate of P
reference soil total
flow of applied manure into occluded P
P content
Initial Level of N
in Field
K in wheat harvest
zero dummy switch
lookup for erosion
rate of N
reference soil N
content
wheat yield residue
adjustment - K
<wheat yield
f(Von liebig)>
Erosion scaling
factor
<manure N
application>
erosion N
flow
residue removal
switch - Wheat only
residue removal
switch
Wheat
DM K
content
English to SI
conversion for
erosivity K
erosion control
practices P
soil
amt Mg/ha
green manure
usage switch
manure K application
manure K content
LS slope factor
%N derived from<green manure DM
N content> erosion
atmosphere
Tarwi green manure N
Manure application - N
denitrification
leaching/
denitrification switch
<Maize yield kg/ha> Lookup for Maize
Response to N
<Fava Bean yield
kg/ha>
Biological N fixation
N in potato harv est
manure P application
Reference
Potato Yield
cover and mgmt
parameter C
<Fava Bean DM N
content>
<green manure
biomass>
fallow vegetation
fixed N
reference
leaching rate
soil erodibility K
Random distribution
limits- erosivity
<wheat yield
f(Von liebig)>
N in wheat harvest
N in Maize harvest
<potato yield
f(Von liebig)>
manure P content
rainfall erosivity R
(English units)
Rainfall erosivity
(SI units)
<residue removal
switch - Wheat only>
Wheat
DM N
content
<amt fixed N in
fallow biomass>
Maize yield residue
adjustment - N
potato
FW N
content
RUSLE
predictors
Wheat yield residue
adjustment - N
N in fava bean harvest
potato DM content
Potato Pulse Shock
potato
yield
kg/ha
Rainfall erosivity
random effect switch
<maize yield
f(Von liebig)>
Maize
DM N
content
Random Impact on
Potato Yield
Random Effect
Switch
<residue removal
switch>
Fava
Bean DM
N
content
manure
application rate
Pulse Time
fallow vegetation
%N in dry matter
Fava bean yield
residue adjustment- N
green manure
biomass
green manure DM
N content
Pulse Duration
Maize
yield
kg/ha
<fava bean yield
f(Von liebig)>
fractional reduction of
manure with
greenmanure use
reference manure
application rate
random impact on
maize yield
Wheat
yield kg/ha
<green manure
usage switch>
<Time>
Random distribution
limits - reference yield
<residue removal
switch>
47
Appendix 3: Materials for table top game:
Appendix 3a: Steps for one round:
Game Guide: How to play each round (year)
1. Choose crops for corresponding year on each field – see field sheets
2. Use manure-u-lator, part 1 to calculate sacks of manure for use on fields
3. Allocate sacks of manure to fields and note allocation on ‘record keeping’ sheet – favoring potatoes, then maize and fava
beans
4. Apply number of NPK beans for each field from the manure-u-lator, part 2
5. Draw a weather risk card for the year to see whether yields of crops will be affected
6. Use crop response tables and the number of NPK beans in each field to find the yield of crops and number of beans to
take out for each field. These go into the ‘harvest’ set of plates
7. For legume crops, add N beans from fixed N (round, grey pigeon peas) to the ‘harvest’ plate according to the crop
response table
8. Reap the benefits or suffer penalties listed on the crop response tables according to yield from each crop
9. Take out beans for erosion according to the erosion levels on your field cards. These go into the ‘erosion’ set of plates.
10. Draw an economic shock card to see whether you suffer an unfortunate event or gain some unseen benefit.
11. Invest in your land or animals using the investment menu. This allows you to lower erosion in one field per round, or buy
or sell animals.
12. Take stock of your animals, money, health, and soil fertility, and return to the next round and start at step one.
48
Appendix 3b: Field sheets and investment menu for setting up the game.
FIELD 1
Fertile, Flat field
Starts with:
30 N
15 P
40 K
Erosion:
HIGH
Loses 8-3-5 per round
MED
LOW
Starts with Erosion
LOW
Rotation
Year
1
2
3
4
5
6
7
8
9
10
Crop choices
Potato
Maize or fava bean or wheat
Wheat or oats or oats/vetch or Tarwi Green Manure
Potato
Potato
Potato
49
FIELD 2
Erosion:
Medium Fertility Field
Starts with:
20 N
12 P
30 K
Starts with Erosion
HIGH
HIGH
Loses 8-3-5 per
round
MED
Loses 5-2-3 per
round
LOW
Loses 1-0-1 per
round
Rotation
Year
1
2
3
4
5
6
7
8
9
10
Crop choices
Potato
Potato
Potato
50
FIELD 3
LOW Fertility Field
Starts with:
15 N
7P
20 K
Starts with Erosion
HIGH
Rotation Start:
Wheat, Oats, or Oats/Vetch
Erosion:
HIGH
MED
LOW
(Oats/Vetch costs 1 money piece)
Rotation
Year
1
2
3
4
5
6
7
8
9
10
Crop choices
Potato
Potato
Potato
51
Investment Menu:
Investment Menu:
In the investment portion of a round, you can In the investment portion of a round, you can
choose any or all of these three:
choose any or all of these three:
Soil Conservation: choose one field for
improvement with barrier plantings or terracing.
This will cost 2 animals and 2 money pieces,
and will lower your erosion 1 level.
Soil Conservation: choose one field for
improvement with barrier plantings or terracing.
This will cost 2 animals and 2 money pieces,
and will lower your erosion 1 level.
Buy animals: buy 4 additional animals at the
cost of 3 money pieces. This will increase the
amount of manure you can apply
Buy animals: buy 4 additional animals at the
cost of 3 money pieces. This will increase the
amount of manure you can apply
Sell animals: sell 3 animals to earn 4 money
pieces. This is a good way to cash in your
‘animal bank account’ if you are short on money.
Sell animals: sell 3 animals to earn 4 money
pieces. This is a good way to cash in your
‘animal bank account’ if you are short on money
52
Appendix 3c: Excel crop sheets for figuring out nutrient exports from fields
Sheets for Potatoes, Maize, and Fava Beans are shown as examples.
53
POTATOES
POTATOES
nutrient beans in
soil
POTATOES
nutrient beans to
subtract
N
P
K
qualitative
fertility
under 20
under 5
under 30
lll
under 20
under 5
30 to 45
llm
under 20
under 5
over 45
llh
under 20
5 to 10
under 30
lml
under 20
5 to 10
30 to 45
lmm
under 20
5 to 10
over 45
lmh
under 20
over 10
under 30
lhl
under 20
over 10
30 to 45
lhm
under 20
over 10
over 45
lhh
20 to 35
under 5
under 30
mll
20 to 35
under 5
30 to 45
mlm
20 to 35
under 5
over 45
mlh
20 to 35
5 to 10
under 30
mml
20 to 35
5 to 10
30 to 45
mmm
20 to 35
5 to 10
over 45
mmh
20 to 35
over 10
under 30
mhl
20 to 35
over 10
30 to 45
mhm
20 to 35
over 10
over 45
mhh
over 35
under 5
under 30
hll
over 35
under 5
30 to 45
hlm
over 35
under 5
over 45
hlh
over 35
5 to 10
under 30
hml
over 35
5 to 10
30 to 45
hmm
over 35
5 to 10
over 45
hmh
over 35
over 10
under 30
hhl
over 35
over 10
30 to 45
hhm
over 35
over 10
over 45
hhh
climate
risk
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
N
P
K
1
1
2
2
2
2
2
1
2
2
2
2
2
1
4
2
4
2
2
2
2
2
4
2
2
2
4
2
4
2
4
2
4
2
6
4
2
2
2
2
4
2
2
2
4
2
4
2
4
2
6
4
6
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
1
0
0
0
0
0
1
0
0
0
1
0
1
0
1
0
1
0
2
1
0
0
0
0
1
0
0
0
1
0
1
0
1
0
2
1
2
1
1
1
3
3
3
3
3
1
3
3
3
3
3
1
8
3
8
3
3
3
3
3
8
3
3
3
8
3
8
3
8
3
8
3
13
8
3
3
3
3
8
3
3
3
8
3
8
3
8
3
13
8
13
8
qualitative
yield
penalty or
benefit
very low
very low
low
low
low
low
low
very low
low
low
low
low
low
very low
medium
low
medium
low
lose 1 health
lose 1 health
lose 1 health
lose 1 health
lose 1 health
lose 1 health
lose 1 health
lose 1 health
lose 1 health
lose 1 health
lose 1 health
lose 1 health
lose 1 health
lose 1 health
none
lose 1 health
none
lose 1 health
low
low
low
low
medium
low
low
low
medium
low
medium
low
medium
low
medium
low
high
medium
lose 1 health
lose 1 health
lose 1 health
lose 1 health
none
lose 1 health
lose 1 health
lose 1 health
none
lose 1 health
none
lose 1 health
none
lose 1 health
none
lose 1 health
earn 1 money
none
lose 1 health
low
low
lose 1 health
low
lose 1 health
lose 1 health
low
medium
none
low
lose 1 health
low
lose 1 health
lose 1 health
low
medium
none
low
lose 1 health
none
medium
low
lose 1 health
medium
none
lose 1 health
low
high
earn 1 money
medium
none
high
earn 1 money and
medium
none
54
MAIZE
MAIZE
nutrient beans in
soil
MAIZE
MAIZE
nutrient beans to
subtract
N
P
K
qualitative
fertility
under 20
under 5
under 30
lll
under 20
under 5
30 to 45
llm
under 20
under 5
over 45
llh
under 20
5 to 10
under 30
lml
under 20
5 to 10
30 to 45
lmm
under 20
5 to 10
over 45
lmh
under 20
over 10
under 30
lhl
under 20
over 10
30 to 45
lhm
under 20
over 10
over 45
lhh
20 to 35
under 5
under 30
mll
20 to 35
under 5
30 to 45
mlm
20 to 35
under 5
over 45
mlh
20 to 35
5 to 10
under 30
mml
20 to 35
5 to 10
30 to 45
mmm
20 to 35
5 to 10
over 45
mmh
20 to 35
over 10
under 30
mhl
20 to 35
over 10
30 to 45
mhm
20 to 35
over 10
over 45
mhh
over 35
under 5
under 30
hll
over 35
under 5
30 to 45
hlm
over 35
under 5
over 45
hlh
over 35
5 to 10
under 30
hml
over 35
5 to 10
30 to 45
hmm
over 35
5 to 10
over 45
hmh
over 35
over 10
under 30
hhl
over 35
over 10
30 to 45
hhm
over 35
over 10
over 45
hhh
climate
risk
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
N
P
K
1
1
2
2
2
2
2
1
2
2
3
2
3
1
3
2
3
2
2
2
2
2
3
2
2
2
3
2
3
2
3
2
5
3
5
3
2
2
3
2
3
2
3
2
3
2
3
2
5
3
5
3
6
3
0
0
1
1
1
1
1
0
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
2
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
2
1
3
1
1
1
3
3
3
3
3
1
3
3
5
3
5
1
5
3
5
3
3
3
3
3
5
3
3
3
5
3
5
3
5
3
8
5
8
5
3
3
5
3
5
3
5
3
5
3
5
3
8
5
8
5
8
5
qualitative
yield
penalty or
benefit
very low
very low
low
low
low
low
low
very low
low
low
none
none
none
none
none
none
none
none
none
none
medium
low
low
very low
medium
low
medium
low
earn 1 money
none
none
none
earn 1 money
none
earn 1 money
none
low
none
low
low
low
medium
low
low
low
medium
low
medium
low
medium
low
high
medium
high
medium
none
none
none
earn 1 money
none
none
none
earn 1 money
none
earn 1 money
none
earn 1 money
none
earn 2 money
earn 1 money
earn 2 money
earn 1 money
low
low
medium
low
medium
low
medium
low
medium
low
medium
low
medium
low
high
medium
high
medium
none
none
earn 1 money
none
earn 1 money
none
earn 1 money
none
earn 1 money
none
earn 1 money
none
earn 1 money
none
earn 2 money
earn 1 money
earn 2 money
earn 1 money
55
FAVA BEAN
FAVA BEAN
nutrient beans in
soil
N
P
K
FAVA BEAN
nutrient beans to
subtract
qualitative
fertility
under 20
under 5
under 30
lll
under 20
under 5
30 to 45
llm
under 20
under 5
over 45
llh
under 20
5 to 10
under 30
lml
under 20
5 to 10
30 to 45
lmm
under 20
5 to 10
over 45
lmh
under 20
over 10
under 30
lhl
under 20
over 10
30 to 45
lhm
under 20
over 10
over 45
lhh
20 to 35
under 5
under 30
mll
20 to 35
under 5
30 to 45
mlm
20 to 35
under 5
over 45
mlh
20 to 35
5 to 10
under 30
mml
20 to 35
5 to 10
30 to 45
mmm
20 to 35
5 to 10
over 45
mmh
20 to 35
over 10
under 30
mhl
20 to 35
over 10
30 to 45
mhm
20 to 35
over 10
over 45
mhh
over 35
under 5
under 30
hll
over 35
under 5
30 to 45
hlm
over 35
under 5
over 45
hlh
over 35
5 to 10
under 30
hml
over 35
5 to 10
30 to 45
hmm
over 35
5 to 10
over 45
hmh
over 35
over 10
under 30
hhl
over 35
over 10
30 to 45
hhm
over 35
over 10
over 45
hhh
climate
risk
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
n
y
N
P
K
1
1
2
2
4
2
2
1
4
2
4
2
4
2
4
2
6
4
2
2
2
2
4
2
2
2
4
2
4
2
4
2
6
4
6
4
2
2
2
2
4
2
4
2
4
2
4
2
4
2
4
2
10
6
0
0
1
1
2
1
1
0
2
1
2
1
2
1
2
1
3
2
1
1
1
1
2
1
1
1
2
1
2
1
2
1
3
2
3
2
1
1
1
1
2
1
2
1
2
1
2
1
2
1
2
1
4
2
1
1
3
3
7
3
3
1
7
3
7
3
7
3
7
3
11
7
3
3
3
3
7
3
3
3
7
3
7
3
7
3
11
7
11
7
3
3
3
3
7
3
7
3
7
3
7
3
7
3
7
3
11
7
FAVA BEAN
qualitative
yield
penalty or
benefit
N fixation
beans, add to
harvest N
very low
very low
low
low
medium
low
low
very low
medium
low
none
none
none
none
gain 1 health
none
none
none
gain 1 health
none
1
1
5
5
9
5
5
1
9
5
medium
low
medium
low
medium
low
high
medium
gain 1 health
none
gain 1 health
none
gain 1 health
none
gain 2 health
gain 1 health
9
5
9
5
9
5
11
9
low
none
5
low
low
low
medium
low
low
low
medium
low
medium
low
medium
low
high
medium
high
medium
none
none
none
gain 1 health
none
none
none
gain 1 health
none
gain 1 health
none
gain 1 health
none
gain 2 health
gain 1 health
gain 2 health
gain 1 health
5
5
5
9
5
5
5
9
5
9
5
9
5
11
9
11
9
low
low
low
low
medium
low
medium
low
medium
low
medium
low
medium
low
medium
low
high
medium
none
none
none
none
gain 1 health
none
gain 1 health
none
gain 1 health
none
gain 1 health
none
gain 1 health
none
gain 1 health
none
gain 2 health
gain 1 health
5
5
5
5
9
5
9
5
9
5
9
5
9
5
9
5
9
9
56
Appendix 3b: Risk cards
Risk cards are cut out and then used for a random draw of the weather risk event (about
half the time there is some level of damage to crops). A similar set of cards govern the
occurrence of economic shocks, and occasionally, economic good fortune.
It’s OK, nothing happened this year
Whew! Another OK year
If you want bad weather, you’re going
to have to wait for next year ☺
57
Hail: Corn, Potatoes, and Fava Beans
affected
Drought:: Oats/Vetch, Fava Beans,
Potatoes, and corn affected.
Early frost: fava bean and potatoes
affected
Hail: only corn affected
Frost: Potatoes affected
affected
affected
Drought:: corn affected
affected
affected
affected
affected
58
Drought:: potato affected
Drought:: potato affected
Appendix 4: Help file from the computer animated game:
Farmer Simulation Game Guide
This guide has a description of the game screen and step-by-step
instructions for how to play.
Top information ba
Crop
selector
Field with N-P-K fertility
status on rollover
Health score
Animal
corral
Money
supply
Total
harvested
nutrients –
N-P-K
Notification and message area
Side bar and
tutorial buttons
Startup and game screen guide:
1. You will be asked to log in or proceed without login. (IMPORTANT: LOGIN
CURRENTLY NOT AVAILABLE AS WE ARE NOT ON A SERVER
59
ANYMORE; USE ‘PROCEED WITHOUT LOGIN) Logging in allows you
to save your score and compare it to your previous scores or other players
if these are logged in. You may wish to practice first without logging in.
To log in you’ll need an internet connection and as simple registration with
the site, so choose ‘proceed without login’ if you are not connected.
2. The game screen has several parts with names indicated in the picture:
1. The play, minimize, and quit buttons are along the very top. The play
button is used to start each round.
2. The top information bar is just below these top buttons. This has the
score for the last round played, the round number, the last round’s
harvested N, P, and K for the field selected with the farmer-pointer,
and the total lost nutrients in the game up to now. One of the objects
of the game is to have a low total lost nutrients, and a high total
harvested nutrients.
3. The crop selector bar in upper left lets you choose a number of crops
typical of the Andes during planting season. Just below the crop
selector, you may also see messages related to what to do next.
4. The money supply symbol at upper right tells you how much money
you have for field investments and unforeseen needs.
5. The health score in the heart at upper right tells you how well your
family is eating and also reflects the impact of health shocks like
illnesses.
6. The animal corral at upper right tells you how many animals you have.
These animals are your source of manure.
7. The fields are large green rectangles numbered one through six. They
are the base of your livelihood as a farmer, and it is your job to
manage them well. At the left side they have a bar showing their
fertility status, and at the bottom, an indicator of the level of nutrient
loss from each field through erosion (High, Medium, Low). If you roll
over a field with the farmer pointer, you can see the nutrient levels for
all three major crop nutrients (Nitrogen-Phosphorus-Potassium). The
game is modeled on the nutrient stocks in 30 x 30 meter fields, in kg of
nutrients.
8. The House at the center of the picture represents your household,
where all the nutrients from your crop harvests are gathered. It shows
the total harvested nutrients for the entire game, which you’d like to be
as high as possible in the interest of your family’s health and income.
9. The bottom left notification area will show messages about events that
affect your crops and household like weather events, illnesses, and
other good and bad luck events.
10. At left, the side bar has a number of links to tutorials and other
websites.
11. Two very useful buttons in the side bar: the finance button shows
information about how the money supply has changed over the game,
60
and the log button shows a complete game log and graph of the
nutrient supplies in your fields.
How to play:
3. Press play, at upper left, to begin planting your crops.
4. There are several parts of the round of play:
1. First you will choose a crop for each of the six fields. The field to be
planted will blink, and you choose a crop by clicking on the crop
button in the crop selector bar at top left. You will plant all six fields,
and choose how much manure to apply in fields that are in the first
two years of the rotation (first year is potatoes and second year is
maize, fava beans, and wheat).
2. At the end of crop selection, you’ll hear a donkey carrying the manure
to each field where you applied manure as the crops are planted.
3. Then the crops will grow. You’ll also see where legumes get their
nitrogen from!
4. You may also have unforeseen weather events that affect the yield of
your crops, and economic shocks that will require you to pay money
or lose health points.
5. At harvest time, low crop yields can affect your health, and high crop
yields can lead to improvements in your money and health status
through better nutrition or selling part of your harvest. Yields from
legumes are especially beneficial to health, as legumes are often
lacking in the diet. The qualitative crop yield (poor, medium, good)
will also appear on each field.
6. In the investments round, you can implement soil conservation to
lower your erosion risk, buy animals for economic security and more
manure, and sell animals if you are short of money.
7. At the end of the round, you will automatically see the log file and the
nutrient status graph that represents the sum of nutrients for all six
fields. This a way of tracking your progress. When the graph is going
down, you are in trouble. If you can make the graph go up over time,
you are doing well.
8. You’ll also see the evaluation card with scores for NPK levels, health,
your NPK efficiency (the proportion of nutrients you are harvesting
versus losing) and the overall score which is a weighted average of
these three.
9. With the sound of the bell, you pass to the next round.
10. You will have opportunities to participate in community efforts like an
irrigation system or a self-help finance group, as the game
progresses.
61
Appendix 5: Materials on macronutrients in soils and crops for the Andean
community of Practice.
Materiales sobre macronutrientes en cultivos y en el suelo para la Comunidad de Práctica
Andina del CCRP, Fundación McKnight
Steven Vanek, Universidad Cornell
Propósito: estos materiales tienen el propósito de dar fundamentos técnicos para entender el rol
de los macronutrientes en los cultivos, su comportamiento en el suelo, e indicaciones sobre su
evaluación con análisis de suelos. Se pretende explicar el rol de estos nutrientes a un nivel que
sea útil para las intervenciones en entrenamiento a grupos de agricultores.
Macronutrientes: los alimentos de las plantas. Los macronutrientes nitrógeno (N), fósforo (P), y
potasio (K) son los alimentos minerales mas importantes para las plantas. N y K pueden alcanzar 2
a 5 % de la materia seca de las plantas, junto con la gran cantidad de carbono que ellas contienen.
El porcentaje de fósforo en las plantas es menor – 0,1 a 0,4% - pero tiene un rol importante y a
muchas veces limitante para los cultivos y por lo tanto se considera un macronutriente.
Para explicar lo elemental de la nutrición humana, los nutricionistas tuvieron la buena idea de nombrar
los alimentos con funciones fáciles de entender. A raíz de este esfuerzo pedagógico, se sabe ahora
en muchas comunidades rurales de Latinoamérica que hay alimentos formadores (proteínicos),
energéticos (carbohidratos), y protectores (vitaminas y fibra). De igual forma podemos entender el
rol de los macronutrientes nitrógeno (N), fósforo (P) y potasio (K) en los cultivos (Fig. 1):
N: formador – junto a carbono, lo fundamental de
la estructura y función de las plantas
P: potenciador energético y reproductivo
K: regulador y protector para responder a estrés
y enfermedades
K+
Fíjese que no hablamos de un alimento
energético en el caso de las plantas, por que ellas
captan energía directamente del sol. De hecho
las plantas son la fuente directa o indirecta de
todos los energéticos que consumimos nosotros,
los otros animales, y también la vida del suelo.
A continuación profundizaremos los roles de N, P,
y K en las plantas y daremos una explicación de
su comportamiento en el suelo.
P
K+
K+
P
K+
N
P
Nitrógeno – formador, esencial para
todas las proteínas:
Rol en el cultivo: Nitrógeno (N) es esencial para
fabricar los ácidos amínicos y, con estos, todas
las proteínas de las plantas. Las proteínas
forman la maquinaria de todas las células, y se
puede entender así tanto la importancia del N
como las grandes cantidades requeridas por las
plantas (hasta 5% de su peso seco). La
maquinaria de la fotosíntesis en las hojas, que
convierte la energía del sol en carbohidratos, es
muy costosa en proteínas y por lo mismo en
nitrógeno. De ahí que un cultivo bien nutrido en N
N
N
K+
P
K+
K+
K
+
Fig. 1: Roles básicos de los macronutrientes:
N como ‘maquinaria’, P como ‘moneda’ de
transferencia energética e impulso
+
reproductivo, K como regulador, protector, y
medio para toda la bioquímica de las células.
Fíjese que el sol juega el rol del ‘alimento
energético’
62
se conoce por un sistema bien integrado de fotosíntesis, que causa un color muy verde en las
hojas de toda la planta. Ausente otras limitaciones de nutrientes, un tallo grande y muchas
hojas con relación a sus raíces es otro ‘síntoma’ de un cultivo bien nutrido en N con capacidad
de armar y integrar toda su maquinaria productiva. En los cereales como trigo que son
adaptados a menos fertilidad, un exceso de nitrógeno puede hacer crecer tanto tallo que se
encamen fácilmente las plantas maduras. De igual manera el terreno de papa con demasiado
nitrógeno produce mucho follaje a costo de la formación de tubérculos y el rendimiento. Las
plantas con deficiencia de N se presentan con hojas amarillas, empezando con las hojas más
viejas de donde se traslada el N hacia el punto de crecimiento. Como se podría esperar de un
elemento ‘formador’, la biomasa total y el rendimiento se reducen con deficiencia de N.
Absorción del suelo: En todas las plantas, N se absorbe por las raíces en forma de iones
disueltos en agua -- el anión nitrato (NO3-) con carga negativa y el catión amoníaco (NH4+) con
carga positiva (Fig. 2). Los cultivos leguminosos (haba, tarwi o chochos, alfalfa, arveja, vicia etc.)
tienen además la posibilidad de formar una simbiosis en las raíces con las bacterias tipo rizóbium
(varias especies y cepas) que les permite fijar o sacar nitrógeno del gran reservorio de N que
conforma la atmósfera. Esta fijación tiene un costo para la planta en forma de carbono que la
planta utiliza para construir nódulos y alimentar a las bacterias. Si hay suficiente luz solar y
condiciones propicias para el crecimiento de una leguminosa, generalmente éste es un costo que
trae a cambio el gran beneficio de nitrógeno para la misma leguminosa, y también los residuos
que benefician a toda la rotación de cultivos.
Leguminosas
Figura 2: Ciclos de nitrógeno en el suelo y los cultivos
(adaptado de Drinkwater, L et el., ‘Ecologically based nutrient management’, en prensa)
63
Comportamiento en el suelo: el nitrógeno se distingue de los otros nutrientes en que sus
reservorios en el suelo y también las transformaciones en el suelo son totalmente productos de
procesos biológicos (Fig.2). La materia orgánica con sus microbios asociados en el suelo es el
único reservorio estable de nitrógeno en el suelo, y puede representar miles de kg por hectárea3
(10-100 kg por 100 m2) de N, que incluye estiércol y residuos recién incorporados, como también
formas muy viejas y estables de materia orgánica. De esta gran cantidad de N, menos que 5%
(1-50 kg/ha o menos que 500g por 100m2) está disponible como nitrato o amoníaco para la
absorción de las plantas en cualquier instante. La transformación del N orgánico hacia la
absorción depende de la descomposición realizada por los microbios del suelo, o de la
descomposición de estos mismos microbios cuando mueren.
La introducción de nitrato o amoniaco de síntesis industrial directamente al suelo con los
fertilizantes es un gran cambio realizado en la agricultura del siglo veinte, que busca sustituir o
aumentar a los ciclos biológicos en la provisión de N a los cultivos. Los procesos industriales
para fijar N en fertilizante requieren mucha energía de hidrocarburos, y es probable que el
fertilizante de N se vuelva cada vez más caro en el futuro. Esta tendencia, mas la falta de
recursos económicos para la compra de fertilizantes en la agricultura campesina motiva la
conservación y desarrollo de conocimientos para optimizar los ciclos y las fuentes biológicas de
N para los cultivos (basados en la energía solar y la fotosíntesis).
El nitrógeno se distingue dentro de los macronutrientes en tener muchas formas o
compuestos en el suelo, algunos de los cuales se pierden fácilmente. El nitrato es muy soluble
en el agua del suelo y se pierde rápidamente durante lluvias grandes que logran saturar y causar
lixiviación o drenaje del suelo. El nitrato también se puede perder mediante la desnitrificación en
periodos de inundación temporánea o cuando hay un suelo con drenaje pobre. La
desnitrificación significa la conversión de nitrato (NO3-) por bacterias a las formas NO2, N2O, y N2,
que son gases y se pierden fácilmente a la atmósfera (Fig. 2). Este proceso se acentúa cuando
hay materia orgánica abundante en el suelo.
A comparación con el nitrato, como catión de carga positiva (NH4+) el amoníaco es más
retenido en la mayoría de los suelos que tienen el complejo de intercambio catiónico, que es el
conjunto de cargas negativas en arcillas y materia orgánica del suelo que atrae a los cationes.
La erosión del suelo causa pérdidas graves de todas las formas de N en el suelo, especialmente
de la materia orgánica particulada (MOP), residuos desmenuzados de todo tamaño, que tienden
a flotar en agua y se pierden más rápidamente en el escurrimiento. El MOP es esencial porque
es una ‘materia prima’ muy importante para los procesos de descomposición y creación de
formas absorbibles de N y también fósforo (P).
Pruebas para N disponible: parece paradójico que pese a la importancia de N para los suelos y
las plantas, es difícil definir una prueba universal que indica precisamente el estado de fertilidad
de N. A continuación se detalla algunas de las pruebas existentes e importantes, junto con sus
limitaciones. Frente a esta falta de una ‘prueba única’, es importante recordar que lo importante
en el manejo de fertilidad de suelos no es responder a un análisis de suelo de una forma
mecánica, sino poner en practica principios y apuntar hacia cualidades del suelo que conducen a
la conservación de nitrógeno y la conversión de nitrógeno de formas orgánicas a formas
disponibles en la época cuando el cultivo lo necesita. Entre estas cualidades serían el nivel de
materia orgánica, la fracción de la materia orgánica que está ‘activa’ o dispuesto a
descomposición y reciclaje, y la capacidad de un suelo de albergar una población de microbios
bien nutridos para hacer funcionar los ciclos de los nutrientes. Tomando en cuenta estos
principios, detallamos a continuación algunas de las pruebas en uso para evaluar disponibilidad
de nitrógeno.
Nitrato instantáneo, o nitrato más amoníaco (Ninorgánico): estas dos pruebas miden el estado
de fertilidad del suelo en el instante del muestreo, indicando cuanto N existe en las dos formas
que utilizan las plantas. Tienen la debilidad que representan una fracción de N en el suelo que
3
Generalmente indicamos las cantidades por hectárea de nutrientes u otros materiales tanto en kg/ha como en kg/100m
(10m x 10m), que puede ser una unidad de terreno mas entendible y relevante para agricultores de la región andina.
2
64
es sujeto a muchos cambios según factores como la absorción de las plantas, la temperatura del
suelo, o la historia reciente de lluvias. Por ejemplo, tarde en el ciclo de cultivo el nivel de nitrato
puede bajarse mucho, lo que no necesariamente indica un suelo pobre. De hecho, un suelo fértil
en nitrógeno es donde se absorbe mucho nitrato para soportar plantas grandes del cultivo, y el
nitrato del suelo puede bajarse mucho por haberse transformado en la biomasa aérea del cultivo.
Sin embargo, esta prueba se ha utilizado bajo pautas estrictas de la época del año, condiciones
de lluvia, etc. para generar datos que ayudan a decidir sobre sí o no fertilizar con nitrógeno de
acción rápida como los fertilizantes. Generalmente es necesario muestrear un suelo justo al
principio del crecimiento exponencial del cultivo (dentro de un mes después de la siembra,
evitando lluvias intensas recientes), y tener cuidado en el manejo de las muestras (mantener frió
o secar al aire inmediatamente después del muestreo, nunca secar en horno). A veces se hace
el análisis con suelo húmedo que evita el efecto de secarse sobre el nivel de nitrato. De esta
forma se logra una estimación del estado de fertilidad de N en ese año. Sin embargo la
replicabilidad de esta prueba se ha puesto en duda.
Prueba de azúcar amínico4: Esta prueba es parecida a la del nitrato en que busca una fracción
del nitrógeno disponible a las plantas, pero diferente porque los azucares amínicos tienen una
permanencia mas larga en el suelo que el nitrato y amoníaco, y sufre menos variación que el
nitrógeno soluble. Los azucares amínicos son moléculas relativamente pequeñas que contienen
N (véase Fig. 2, lado izquierda para las ‘moléculas orgánicas sencillas’). Se piensa que el éxito
demostrado de esta prueba tiene que ver con el hecho de que estas moléculas son realmente la
‘materia prima’ mas inmediato a la mineralización de N de la materia orgánica hacia formas
disponibles a las plantas, que puede pasar en las semanas o meses posterior a la medición, o
sea con relevancia para el cultivo de la misma temporada agrícola. En la practica la prueba es
mas difícil que medir el nitrato, ya que hay que hacer una digestión en acido fuerte, mas realizar
los análisis de azucares amínicos en el extracto.
N total en el suelo: junto al análisis de nitrato, es el análisis para nitrógeno más común. Se
utiliza frecuentemente como parámetro para describir lugares o suelos en la investigación, o en
el modelaje, por su simplicidad, relativa estabilidad, y relación con la cantidad de materia
orgánica. Por esta relación con la materia orgánica, se puede utilizar también el porcentaje de
materia orgánica como ‘indicador’ de fertilidad de nitrógeno. La debilidad de estos porcentajes
totales es que solo una fracción mínima del nitrógeno total se libera para la producción de los
cultivos. La cantidad de nitrógeno en un suelo puede ser enorme, pero características como la
calidad del N y su edad (vieja vs. nueva, residuos frescos vs. materia humificada, residuos de
leguminosas vs. los de cereales, etc.) afectan fuertemente el porcentaje de N total que se
moviliza de la materia orgánica en un año determinado, que muchas veces es el parámetro mas
interesante para los agricultores. Sin embargo, tener suficiente N total o cantidad de materia
orgánica total es un componente necesario para dar suficiencia sostenida de nitrógeno.
Incubaciones y bioensayos: otro análisis que resuelve muchos de los problemas en medir N
disponible es incubar un suelo bajo condiciones de humedad y temperatura ideal para la
mineralización o descomposición (60% de la capacidad de campo, a 26° a 30°C) Durante la
incubación, o a su final, se mide la cantidad de nitrato y amoniaco liberada en la descomposición
y se utiliza esta cantidad como una medida de fertilidad del suelo. El único defecto de este
análisis es el tiempo requerido y su procedimiento largo. Se utiliza esta técnica en la
investigación para hacer una caracterización de un suelo referente a fertilidad de nitrógeno.
Otra técnica parecida es instalar un bioensayo, con plantas no leguminosas en macetas
o en campo abierto, para medir la absorción de N por una planta ‘indicador’. Los bioensayos son
un buen enfoque para los cursos con agricultores o profesionales, aunque hay que tener cuidado
para que otros factores (compactación en las macetas, sequía, otros nutrientes etc.) no tengan
efectos negativos confundidos con la falta de fertilidad del nitrógeno.
Balanza de nutrientes para N: se puede también tomar un enfoque más sistémico y medir o
estimar las entradas de nitrógeno en forma de fijación biológica, estiércol, enmiendas, y
fertilizante, y también las salidas como cosecha y erosión. La diferencia entre entradas de N y
salidas de N a través de una rotación de cultivos, dará una aproximación a la tendencia de un
4
Vease Mulvaney, R.L., S.A. Khan, R.G. Hoeft, y H.M. Brown. 2001. A Soil Organic Nitrogen Fraction that Reduces the
Need for Nitrogen Fertilization. Soil Science Society of America Journal 65: 1164-1172.
65
suelo de acumular o perder fuentes de nitrógeno disponibles. Hay que tomar en cuenta también
el estado inicial del suelo. Para un suelo que ya está fértil en nitrógeno, la balanza debería ser
cero o levemente positiva para balancear los mecanismos de pérdida. Al contrario, un suelo
pobre va a necesitar un aumento positivo significativo, con el uso de enmiendas o abonos verdes
junto al uso posible de fertilizantes, para ganar productividad. Si la balanza indica una perdida
grave de nitrógeno a través del tiempo, hay que pensar en como aumentar las entradas
(leguminosas con fijación biológica, estiércol, enmiendas como compost) o reducir las perdidas
(erosión es la perdida mas importante en muchos casos)
El gran número de formas de N en el suelo y los posibles errores utilizando una sola
prueba para evaluar la fertilidad de N nos conduce a un tipo de medición integrado de estatus de
N que se asemeja a los criterios de los mejores agricultores. En general, un suelo bien
agregado, con suficiente materia orgánica, residuos frescos (mas en algunos casos fertilización
leve), y poblaciones de microbios recicladores de nutrientes, con porosidad, humedad, aeración,
y temperatura adecuado, será capaz de proveer cantidades suficientes de N y una relación
costo:beneficio provechoso. La estrategia adecuada en muchos casos también tendrá que
remplazar una agricultura de exportación con una agricultura de conservación y construcción de
suelos, haciendo mas positivas a las balanzas de nutrientes y materia orgánica.
Fósforo: ‘potenciador energético y reproductivo’
Rol en el cultivo: además de ser esencial para la construcción de membranas de las células, y
para la fabricación de ácidos nucleicos para el código genético de todo ser vivo, el fósforo (P)
cumple la función en los cultivos de una ‘moneda’ energética para la función de casi todo los
procesos enzimáticos. Por ejemplo, es esencial para las enzimas que manejan la síntesis y
‘empaquetado’ de carbohidratos en las hojas de las plantas cuando la planta hace la fotosíntesis.
Por eso cuando falta P hay una acumulación de almidón en las hojas y una fotosíntesis poco
eficiente, que no permite al tallo de la planta crecer como debería. En general, por su rol
energético y su rol en los ácidos nucleicos y membranas, el fósforo es más abundante en los
puntos de crecimiento y floración de la planta, y el fósforo se relaciona fuertemente con los
aspectos reproductivos y de ramificación de la planta. Una planta que está bien nutrida con
fósforo florece más temprano y tiene la posibilidad de producir más y mejores flores y semillas.
En los cereales, un cultivo bien nutrido produce abundante macollaje (tallos múltiples en la
misma planta); al contrario cuando hay un suelo deficiente en fósforo disponible, se produce
cereales con ‘una paja por planta’, un aspecto que puede ser notado fácilmente al pasar y hasta
después de la cosecha. Como ejemplo de un cultivo dicotiledóneo, en los frijoles una planta bien
nutrida produce abundante ramificación y floración temprana. Los frijoles y otras plantas
deficiente fósforo tiene poca ramificación (hasta un solo tallo, sin ramificación), floración poca y
tardía, e internudos muy cercanos el uno al otro. Un color morado en los bordes de las hojas
viejas es una síntoma que aparece en varios especies como tomates, cereales, y maíz (en maíz
tiene que distinguirse del color morado de ciertas variedades)
La fijación de N es costoso en energía para las leguminosas, y el fósforo es muy
importante para la fijación de nitrógeno por parte de la simbiosis leguminosa-rizóbium. Cuando
hay limitaciones de fósforo, frecuentemente se logra un aumento en la fijación agregando
fertilidad de fósforo en forma de estiércol, fertilizante, o roca fosfática.
Absorción del suelo por el cultivo: Tal como el nitrógeno, el fósforo (P) es absorbido por las
plantas en la forma de H2PO4-, un anión soluble (Pinorgánico). Pero la concentración de P soluble
en el agua del suelo es bajísima en muchas situaciones, y las plantas han desarrollado una serie
de adaptaciones para buscar mejor el P soluble, y dirigir fuentes de P insoluble hacia ellos.
Primero, la deficiencia de P provoca que una planta detenga el desarrollo de su tallo pero sigue
desarrollando su sistema de raíces, una adaptación que le permite a un cultivo corregir el
desequilibrio entre sus fuentes y su uso de fósforo. Hay también factores de genotipo de los
cultivos en su sistema radicular, como abundante raíces superficiales, más raíces por
bifurcación, y adaptaciones de los pelos absorbentes que permite explorar el suelo más
eficientemente5. Hay también fosfatazos producidas por las raíces y por los microbios de la
5
Lynch, J.P. 2007. Roots of the Second Green Revolution. Australian Journal of Botany 55: 493-512.
66
rizósfera que se alimentan de las excreciones de las raíces, lo que permite atacar a fuentes de
fósforo orgánico disponible y generar Pinorgánico. Las micorrizas u ‘hongos-raíces’ son una
adaptación asombrosa de las raíces y los hongos del suelo, en que una raíz se infecta con
hongos, los que le permite explorar un volumen mucho más amplio de suelo mediante las hifas
del hongo y encontrar el poco P disponible que hay. Durante esta simbiosis se genera en la raíz
sitios de intercambio de nutrientes, donde la planta ‘paga’ al hongo por el fósforo (y
micronutrientes - Zn, etc.) con carbohidratos sintetizados en las hojas con la energía solar.
Figura 3: los subciclos biológicos y geoquímicos de fósforo en el suelo y los cultivos.
(Adaptada de Drinkwater, L. et el., ‘Ecologically based nutrient management’, en prensa)
Comportamiento en el suelo: como ya se ha mencionado, la concentración de Pinorgánico en el agua
del suelo es generalmente muy baja, debido a las interacciones entre fósforo y varios componentes
del suelo que tiene la capacidad de absorber y fijar el fósforo, haciéndolo menos accesible a la
planta. En comparación con el nitrógeno, cuyos ciclos en el suelo son dominados por factores
biológicos, los ciclos de P en el suelo tienen aspectos puramente geoquímicos y también biológicos
(Fig. 3). La concentración de P soluble sufre reducciones por las siguientes interacciones químicas:
Adsorción (Fig. 4.1): En los suelos, a una escala microscópica, superficies con carga positiva,
agarre de una forma temporánea a los aniones de fósforo PO4-. Estas superficies pueden ser
óxidos de hierro y aluminio, o también ciertos bordes de las ‘placas’ que tienen los cristales de
las arcillas6. Los vínculos entre estas cargas positivas y los aniones son relativamente débiles, y
el P adsorbido de esta manera puede hacerse disponible a las plantas.
6
La mayoría de las superficies en las arcillas tienen carga negativa, no positiva, y contribuyen a la
capacidad de intercambio catiónico de las mismas. Sin embargo existen lugares en los bordes, donde se
terminan las placas que tienen cargas positivas y las permite adsorber aniones como el fosfato.
67
Absorción o penetración (Fig. 4.2): los mismos óxidos y otros minerales del suelo, puede
recibir a los aniones de fósforo y dejarlos entrar mas profundamente en su estructura, hasta que
su difusión o disponibilidad queda prácticamente nulo.
Oclusión (Fig. 4.3): ya siendo en un estado absorbido, los óxidos de hierro y aluminio puede
formar un tipo de capa selladora en los minerales del suelo que contienen fósforo. A partir de
esta oclusión prácticamente se pierde cualquier interacción con procesos del suelo y cualquier
disponibilidad por las plantas.
Figura 4. Mecanismos que reducen la cantidad disponible de fósforo en muchos suelos.
(Fig. 4.2 y 4.3 tomadas de Cornforth, I.S. 2007 ‘The fate of phosphate fertilizers in soil’. Acceso electrónico a
http://www.nzic.org.nz/ChemProcesses/soils/2D.pdf , 2 febrero 2008)
4.1. Adsorción: los aniones de fosfato se encuentran temporalmente atrapado en la
superficie de un mineral de oxido de hierro o aluminio. Se ve que las cargas negativas de
los aniones se juntan con las cargas positivas de la superficie.
Aniones de fosfato (H2PO4-)
Superficie de óxido de
hierro o aluminio
(M= hierro o aluminio)
Superficie de óxido
de hierro o aluminio
Figura 4.2. Absorción o penetración: los aniones de fosfato entran paulatinamente a un
grano de mineral (óxido, arcilla, etc.), donde son mucho mas difícil de sacar por las raíces
u microbios del suelo.
Aniones
de fosfato
(P) en
solución
grano de
mineral en
el suelo
68
Figura 4.3 Oclusión: fósforo adsorbido se encuentra atrapado por la formación de una
capa de óxidos sobre el grano de mineral en el suelo. El fósforo ocluido es muy difícil de
sacar y se considera fuera de acceso para las plantas.
Capa de
óxido de
hierro o
aluminio
grano de
mineral en
el suelo
Fósforo
atrapado
Figura 4.4. Ejemplo de precipitación en un suelo con pH neutro y calcio soluble. Cationes
de calcio se combinan con aniones de fosfato para formar fosfato de calcio en forma
cristalina. Un catión de calcio tiene carga positiva +2, por lo que se une con dos aniones
de fosfato (carga -1).
Ca++
Aniones de
fosfato en
solución
Ca++
Ca++
Cationes de
calcio
Ca++
Ca++
Ca++
Mineral de fosfato de calcio
Precipitación de fosfato de calcio
Ca++
Ca++
Ca++
Ca++
a++
Ca++
69
Precipitación (Fig. 4.4): Antes los científicos del suelo pensaban que la precipitación7 de
fosfatos de aluminio y hierro a pH bajo, y fosfatos de calcio a pH alto, era muy importante en
mantener baja la cantidad de fósforo soluble en un suelo. Actualmente se piensa que los
procesos de adsorción, absorción, y oclusión detallados arriba son los principales causantes de
la baja disponibilidad de P en los suelos. Sin embargo la precipitación de fosfatos de calcio en
los suelos con pH 6,5 por arriba es un fenómeno importante, especialmente cuando hay calcio
soluble en un suelo calcáreo.
Suelos alofánicos: Los suelos andosoles que se forman en regiones volcánicas como la sierra
Ecuatoriana y partes del Perú y Bolivia, se desarrolla a base de un substrato de ceniza volcánica
y tiene un alto contenido de silicatos amorfos de aluminio. La presencia de estos silicatos implica
un potencial fuerte de adsorción y absorción de fósforo8. Los andosoles pueden tener un alto
contenido de materia orgánica, y se da la paradoja de un suelo que parece fértil, oscuro, y con
alto contenido de fósforo total, sin mucho fósforo disponible y serios problemas de fertilidad.
Factores biológicos en la disponibilidad de fósforo del suelo:
La importancia de la materia orgánica
Hay por lo menos tres razones por la cual un aumento de materia orgánica en el suelo
generalmente tiene el efecto de combatir los efectos de sorción mencionadas arriba y aumentar
la cantidad de fósforo disponible:
1. A comparación con el fósforo sorbido químicamente en arcillas y óxidos, los compuestos
orgánicos de fósforo en el suelo9 provenientes de los residuos de plantas y microbios son de
acceso mucho mas fácil para la descomposición y la absorción de las plantas y micorrizas, por
que son el sustrato para las reacciones de las enzimas fosfatazos y otras exoenzimas de las
raíces y los microbios (véase Fig. 3).
2. Las partículas complejas de materia orgánica humificada (bien descompuestas) tienen cargas
negativas, que logran vincular con los óxidos de hierro y aluminio en el suelo y ‘bloquear’ sus
sitios de sorcion de P disponible. Esto resulta en un aumento de P disponible.
3. La materia orgánica está asociada con una población apreciable de microbios alimentados por las
fuentes de carbono en los residuos. Estos microbios forman en sí una cantidad de fósforo – hasta 60
kg/ha (600g por 100 m2) en algunos suelos. Este fósforo se hace disponible con la muerte y
descomposición de los microbios, cuyas poblaciones se reciclan constantemente.
Pruebas para P disponible:
Como ya se señaló, la cantidad de P soluble en el suelo en cualquier momento es muy baja y no
representa bien las cantidades adicionales disponibles a los cultivos, por lo que no es un buen
parámetro para la medición de P disponible. Al otro extremo, el P total en un suelo no es útil
como indicador de fertilidad porque mucho de este P puede ser fijado u ocluido en formas
indisponibles. Para indicar la fertilidad de fósforo de los suelos, se han desarrollado diferentes
extracciones que indica o ‘revela’ las fuentes de fósforo no soluble pero accesible para las
plantas. Pero, las diferentes extracciones dan respuestas diferentes, por utilizar reactivos mas o
menos fuertes en la extracción. Mas aún, para indicar realmente las necesidades de un cultivo,
los diferentes análisis generalmente tienen que ser calibrados a nivel nacional o regional, con
investigación en cultivos que categoriza cuando hay o no hay respuesta del cultivo a la fertilidad
aplicada. Esta ‘investigación de calibración’ para las pruebas del suelo es escasa en la región
andina. Sin embargo, sabiendo los rangos de deficiencia y suficiencia de las diferentes pruebas,
se puede saber algo de la fertilidad de un terreno, correlacionar con conocimiento local sobre
cuales terrenos son fértiles, o por lo menos comparar dos terrenos en cuanto a su nivel de
fósforo. A continuación se detalla las principales pruebas en uso actualmente, sus rangos de
deficiencia y suficiencia, y sus limitaciones. En la consultación de los datos de análisis de
7
(formación de un mineral de por si, desde componentes disueltos en agua)
Para explicar esta tremenda capacidad de sorción, se puede recordar que los bordes de las ‘placas’ de
arcillas silicatos son lugares de sorcion de P (intercambio aniónico), a contraste de las placas mismas que
son lugares de intercambio catiónico. Por su falta de estructura regular, un silicato amorfo de aluminio
actúa como una arcilla con ‘100% bordes’ – poca estructura de placa, y una estructura como una esponja
con muchos espacios con cargas de superficie positivo y posibilidad de absorber aniones como el H2PO4 .
9
fitates, ácidos nucleicos y otros
8
70
suelos, es muy importante averiguar con un laboratorio de análisis, el nombre del análisis
utilizado para nitrógeno, fósforo y los demás nutrientes.
Tabla 1. Diferentes pruebas de fósforo con sus rangos de suficiencia según
investigaciones en suelos de Iowa, Estados Unidos.10
Resultados en ppm (igual a mg/kg)
Prueba
Muy Bajo
Bajo
0-5
6-10
Olsen
0-8
9-15
Bray 1
0-8
9-15
Mehlich 3
Optimo
11-14
16-20
16-20
Alto
15-20
21-30
21-30
Muy Alto
>20
>30
>30
Prueba Olsen: El llamado ‘P Olsen’ se mide utilizando una solución de extracción de
bicarbonato de sodio a un pH de 8,5. Es una extracción no muy fuerte y por lo mismo da
números para suficiencia y deficiencia generalmente bajos. Los rangos deficientes, óptimos, y
altos de la prueba Olsen se indica en la tabla 1. A un pH del suelo por debajo de 5 (un suelo
muy acido acercándose a pH 4, por ejemplo) la prueba Olsen es menos confiable.
Prueba Bray 1 y 2: La prueba Bray 1 se utiliza en muchos lugares. La única diferencia de la
prueba Bray 2 es que lleva un ácidez más fuerte y extrae más fósforo. Ambas soluciones
contiene fluoruro de amoníaco para extraer el fósforo. Como son extracciones fuertes, producen
resultados más elevados que la de Olsen, y sus rangos de suficiencia también son más
elevados. A un pH del suelo mayor a 7,4 las pruebas Bray dan resultados muy variados y no se
deben utilizar.
Prueba Mehlich-3: La prueba Mehlich-3 (M3) utiliza una mezcla de reactivos en su extracción,
incluidos ácido acético, Fluoruro de amoníaco, EDTA, ácido nítrico y nitrato de amonio. La
ventaja es tener una sola extracción para una serie de elementos en el suelo (fósforo y potasio,
calcio, magnesio y los micronutrientes metales). Otra ventaja es que el pH del suelo no afecta
tanto a los resultados del M3 que a las otras pruebas. En términos de su poder extractivo, la M3
es similar a la prueba Bray y tiene los mismos rangos de suficiencia (Tabla 1).
Potasio: ‘regulador y protector en los cultivos’
Rol en el cultivo: a diferencia de nitrógeno y fósforo que se incorporan a las moléculas o
estructura orgánica de las plantas, el potasio (K) siempre queda en forma de un catión (K+),
como un sal disuelto en agua dentro de la planta, siendo el catión de mayor concentración en la
savia de las células. Si el nitrógeno representa la maquinaria de las células, y el fósforo su
‘moneda energética’, el potasio representa un tipo de lubricante que mantiene bien equilibrados
los procesos, y también participa en la comunicación y control de la maquinaria. El potasio
disuelto en el agua de las células regula el pH para que funcionen bien las enzimas. Como el
interruptor que pone en marcha a la maquinaria de las células, el potasio también tiene el rol de
activar muchas enzimas de las plantas. Es importante además para cargar los carbohidratos
(azúcares) que provienen de la fotosíntesis al floema (las ‘venas’ de las plantas). Como sal
disuelto en las células, ayuda a generar la presión osmótica de agua que para y hace rígidos los
tejidos blandos de la planta. Tambien regula los procesos de expansión y crecimiento de
células.
Tal vez la función más importante de potasio, para agricultores andinos que cultivan en
secano y con riesgo de temperaturas bajas, es que ayuda a la planta a regularse frente a los
extremos de la sequía y las heladas. Por ejemplo, una cantidad suficiente de K permite a la planta
mantener la presión dentro de las células y evitar el marchitamiento de la planta hasta un extremo
10
Reproducido de Sawyer, J.E. y Mallarino, A.P. ‘Differentiating and Understanding the Mehlich 3, Bray, and Olsen
Soil Phosphorus Tests’ . Disponible en http://extension.agron.iastate.edu/soilfertility/info/mnconf11_22_99.pdf
71
de sequía mas fuerte que cuando no hay suficiencia de potasio. El potasio también tiene un rol
fuerte en hacer correr la fotosíntesis, y una planta suficiente en K mantiene mejor su producción de
carbohidratos bajo condiciones de sequía que una planta deficiente. Además el K+ es importante
en la regulación de pequeñas aperturas en las hojas, las estomas, que controlan la entrada de
gases y la pérdida de agua de las hojas. Suficiencia en potasio hace las hojas más eficientes en
regular la perdida de agua. Frente a una helada, la planta busca salvarse concentrando la solución
dentro de sus hojas y tallos, un fenómeno que se asemeja mucho a su respuesta a la sequía. Se
ha visto en experimentos con papa que el K se hace importante en reducir el daño de las heladas
(tabla 2).
Tabla 2: Rendimiento de papa y porcentaje de follaje dañado por helada, promedio de 14
experimentos11
K aplicada en
fertilizante
(kg/ha)
0
42
84
Rendimiento de papa
(ton/ha)
2,39
2,72
2,87
Contenido de K en
hojas (% de peso
seco)
2,44
2,76
3,00
Porcentaje de daño a
las hojas por la
helada
30
16
7
Las plantas deficientes en fósforo tienden a mostrar el estrés de la sequía más rápido, con
marchitamiento fácil. Manchas claras y necrosis (tejido muerto) en el borde de las hojas viejas
es otro síntoma común entre muchas especies. En maíz se produce un aspecto seco o
quemado en los bordes de las hojas, y en fríjol aparecen manchas claras y necrosis (tejido
muerto) en el borde de las hojas viejas. En las leguminosas de hoja pequeña como alfalfa y
vicia, aparecen puntitos blancos en los márgenes de las hojas, que fácilmente se confunden con
el daño de un insecto. La deficiencia de K se puede distinguir de la salinidad porque la salinidad
mostrará síntomas en las hojas nuevas, la deficiencia de potasio, en hojas viejas. Por su rol en
el transporte de productos de fotosíntesis en la planta, deficiencia en K puede perjudicar la
producción de semillas y tubérculos grandes. En los cereales otro síntoma son tallos débiles que
se acaman fácilmente, pero no a consecuencia de una planta alta y ‘sobre-nutrido’ como es el
caso con el exceso de nitrógeno. La deficiencia en K también le vuelve a las plantas más
vulnerables a los ataques de las enfermedades.
Un hecho interesante y relevante para la región andina es que la quínoa, y otros plantas
de su familia (betarraga, etc.) y algunas otras familias de plantas, pueden sustituir parte de su
requerimiento de potasio (K+) con sodio (Na+) Se piensa que ésta es una adaptación a los
suelos sódicos como los que se encuentran alrededor de los salares de Bolivia, donde se cultiva
mucha quínoa.
Comportamiento en el suelo: El potasio que contiene las piedras silicatos y minerales de arcilla
en muchos suelos forma un gran reservorio de potasio no disponible, que se transforma poco a
poco con procesos de alteración geológica a la forma iónica K+ (Fig. 3). Ya como catión, el
potasio se conserva moderadamente disponible en el suelo en el complejo de intercambio
catiónico. Este complejo no es un lugar específico, sino el conjunto de cargas negativas que
tiene la arcilla y la materia orgánica en el suelo que atrae a los nutrientes cationes como K+, Ca++
(calcio), Mg++ (magnesio) y NH4+ (amoníaco). De este complejo, los cationes de potasio se
liberan para entrar a las raíces de los cultivos. Igual que su comportamiento en las plantas, el
potasio disponible en suelo siempre está en forma del catión K+, asociado con agua o retenido
en complejo con superficies de carga negativa.
11
Reproducido de Marschner, H 1995. Mineral Nutrition of Higher Plants.
72
Figura 5: ciclos de potasio en el suelo. La gran mayoría del potasio en el suelo (>95%) se
conserva en los minerales primarias y el potasio fijado entre capas de arcillas.
Por el gran reservorio que existe en muchos suelos (exceptuándose los suelos muy antiguos, por
ejemplo los oxisoles de los bosques tropicales) y por los mecanismos del complejo de intercambio
de cationes, que conserva el potasio en formas medianamente disponible en el suelo, el potasio es
el macronutriente que generalmente tiene la menor incidencia de deficiencia aguda. Sin embargo,
cuando se cosecha cultivos mas sus residuos de un terreno a través de muchos años sin dejarlo
descansar o aplicar fuentes de potasio, las salidas constantes conducen a un agotamiento lento
pero seguro de potasio en cualquier suelo, especialmente en los que no tienen grandes cantidades
de arcilla. Para dar una idea, un cultivo de lechuga extrae solo 40 kg por hectárea de potasio del
suelo, pero un cultivo de papa fácilmente extrae 150 kg de K por hectárea, y un cultivo de alfalfa
con varios cortes hasta 400 kg/ha de potasio. 150 o 400 kg/ha de potasio supera lo que
generalmente se aplica por un campesino en estiércol o fertilizante, llevando a una balanza
negativa. El agotamiento de potasio es un factor que fácilmente puede afectar a muchos
productores campesinos en la región andina, dada la importancia de tubérculos como papa y la
tendencia de exportar los residuos como paja o tallos de maíz, donde frecuentemente se queda
bastante potasio, para alimentar a los animales.
Pruebas para disponibilidad en suelos:
Las dos pruebas que se utiliza para potasio son parecidas a las pruebas de fósforo, en cuanto a
que se aplica una solución de extracción para ‘revelar’ las fuentes de potasio solubles y también
los que se hacen disponibles a las plantas como parte del complejo de intercambio catiónico.
Las dos pruebas que se utiliza mundialmente son la de acetato de amonio, que se desarrolló
hace mucho tiempo, y el Mehlich-3 que es más nuevo. Las dos utilizan el catión amonio en
concentraciones bastante grandes para desplazar cualquier otro catión del complejo de carga
catiónico (incluidos K+ Mg++, Ca++, K+, los llamados cationes alcalinos). A continuación en la
73
tabla 3 se presenta los rangos de suficiencia para potasio de Iowa, un estado de Estados Unidos.
Hay que leer esta tabla con precaución, ya que es producto de investigación de cultivos y suelos
específicos y de carácter regional y no global. Sin embargo da ciertas pautas para lo que podría
considerarse niveles muy bajas, medianas, o muy altas.
Tabla 3: rangos de suficiencia para las pruebas de acetato de amonio o Mehlich-3 de
investigación en Iowa, EE. UU.12 Se nota que los dos análisis dan rangos idénticos.
Resultados en ppm o mg/kg
Prueba
Muy Bajo
Acetato de
0-90
amonio
0-90
Mehlich 3
Bajo
Optimo
Alto
Muy Alto
91-130
131-170
171-200
>200
91-130
131-170
171-200
>200
Conversión de resultados de análisis de potasio en meq./100g (=cmol/kg):
Algunos laboratorios dan sus resultados de estos análisis, no en ppm (el mismo que mg/kg) de suelo seco,
sino en meq./100g o cmol/kg (los dos son iguales), que son medidas de la cantidad de cationes de potasio
en cada 100g (o kg) de suelo seco. Este sistema de unidades se utiliza para calcular la ‘saturación alcalina’
++
++
+
de un suelo, o el porcentaje del complejo de cationes compuesto por los cationes alcalinos Ca , Mg , y K ,
que resulta ser relacionado con aspectos de pH y fertilidad. Para convertir los resultados de meq./100g o
cmol/kg (que son unidades idénticas) a ppm o mg/kg es necesario multiplicar por 391♣. Por ejemplo si el
laboratorio da un resultado en meq./100g de 0,18, la cantidad de potasio en ppm de suelo seco es 0,18 por
391 = 70,4 – que se califica en la tabla arriba como muy bajo.
12
Reproducido de Sawyer, J.E. y Mallarino, A.P. ‘Differentiating and Understanding the Mehlich 3, Bray, and Olsen
Soil Phosphorus Tests’ . Disponible en http://extension.agron.iastate.edu/soilfertility/info/mnconf11_22_99.pdf
♣
Este factor se determina utilizando el peso atómico de K, 39.1 g/mol. Haciendo la conversión de cmol/kg a mg/kg
introduce otro factor de multiplicar por 10:
(cmol/kg) x (39.1g/mol) x (1000 mg/g) / (100 cmol/mol) = 391 [(mg/kg)/(cmol/kg)]
74
Tabla 4: Resumen de roles y comportamiento de macronutrientes en plantas y suelos.
Porcentaje de materia
seca de los cultivos
Rol en el cultivo
Nitrógeno (N)
Fósforo (P)
Potasio (K)
1% - 5%
0,1% - 0,4%
2%-5%
‘Potenciador energético y reproductivo’ – importante
para la transferencia de energía en las plantas.
Generalmente impulsa la ramificación y la reproducción.
‘Regulador y Protector’ – como un
medio disuelto en el agua de las células,
importante para defender contra sequía,
enfermedades, y heladas. Regula los
procesos enzimáticos como el llenado de
granos y tubérculos.
‘Formador’ – utilizado en la
construcción de cualquier proteína o
enzima. La maquinaria de la
fotosíntesis es especialmente
costosa en proteínas.
-
Absorción del suelo
por la planta
En forma de iones de nitrato (NO3 ) o
amoníaco (NH4+) que resulta de la
descomposición de materia orgánica
o residuos, o de fertilizante agregado.
Formas en el suelo
Diversas iones y moléculas; mas que
95% en el suelo como materia
orgánica con diferentes edades
desde residuos frescos hasta
moléculas complejas y muy antiguos
Limitaciones de
acceso para las
plantas
Hay limitaciones cuando hay
insuficiente residuos o residuos
demasiado pobres en nitrógeno (alta
relación carbono:nitrógeno)
Adaptaciones
especiales de las
plantas para acceso
Se piensa que las plantas
efectivamente ‘alimentan’ algunos
microbios en la zona de las raíces,
para estimular la descomposición y
liberación de nitrógeno en forma
inorgánica.
-
2En forma de iones de fosfato (H2PO4 y HPO4 ) que se
libera paulatinamente de fuentes fijados en el suelo.
También por transferencia de hongos micorrizas
simbióticos.
•
•
•
•
Iones de fosfato, concentración muy baja
Adsorbido en las superficies de los minerales
Penetrado hasta el interior de los minerales
Ocluido o atrapado por capas de óxidos de hierro o
aluminio.
• Minerales de fósforo en granos de arena o lima,
como la roca fosfática o fosfatos de calcio
• Formas orgánicas en residuos y biomasa de
microbios
Las formas penetrados y ocluidos son prácticamente
imposibles de utilizar para las plantas. Las formas
adsorbidos y los minerales de fósforo, mas las
formas orgánicas, son de acceso mediano a difícil.
Acceso difícil en suelos alofánicos o andosoles.
• Arquitectura de raíces adaptado para explorar y
explotar mejor el volumen del suelo
• Micorrizas que aumentan el volumen explorado para
fósforo
• Exoenzimas que hacen descomponer la materia
orgánica para liberar iones de fosfato.
• Exudación de ácidos orgánicos de las raíces para
disolver vínculos entre óxidos y fósforo adsorbido.
Existe tanto en el suelo como en la
planta como el cation K+. Se absorbe a
las raíces en esta forma.
Gran reservorio en minerales silicatos
todavía sin descomponer;
Fracciones atrapados en arcillas (no
disponible a plantas) que se transforma
lentamente en fracción disponible o
intercambiable en el complejo de
intercambio catiónico.
Solo se presenta limitaciones en las
siguientes condiciones:
• Suelos sin mucho contenido de arcilla
(arenosos, generalmente)
• Rotaciones de cultivos con mucha
extracción de biomasa (papas,
residuos, heno, alfalfa p. ej)
• Suelos muy antiguos o lixiviados
Por la falta de limitación en muchos
sistemas, no hay muchas adaptaciones;
no es un tema muy estudiado.
75
• Acidificación de la zona de las raíces, para disolver
minerales originales y precipitados de fósforo.
Tabla 4 (continuación)
• Guano o estiércol
• Abonos verdes, especialmente leguminosos que
tienen altas concentraciones de P en sus tejidos
• Roca fosfática
• Fertilizantes fosfatadas.
Guano o estiércol; ceniza; fertilizantes
potásicos.
Fuentes de fertilidad
Guano o estiércol; abonos verdes
leguminosos; residuos de cultivos,
especialmente leguminosos;
fertilizantes con amonio, nitrato, o
urea.
Estrategias para
aumentar
disponibilidad
Más que para otros nutrientes,
optimizar los ciclos biológicos de
fijación de nitrógeno y
descomposición de estiércol y
residuos para sincronizar con la
necesidad del cultivo; controlar la
erosión.
Aumentar niveles de materia orgánica con residuos o
estiércol; Promover leguminosas en las rotaciones;
encalado en suelos ácidos para neutralizar óxidos de
hierro y aluminio. Uso de cultivos tradicionales o
mejorados con eficiencia y adaptaciones a P bajo (tarwi,
por ejemplo); controlar la erosión.
En la rotación, alternar cultivos
esquirmantes o extractadores (p. ej.
papa, maíz con su residuo mas) y los
con baja exportación (p. ej) cereales sin
cosechar su residuo); limitar la erosión;
agregar fertilidad.
Análisis de suelo para
medir
Nitrato, bajo normas estrictas de
época de crecimiento del cultivo.
N Total, para comparación general
con otros lugares. (se puede utilizar
materia orgánica total también)
Incubaciones, bioensayos, y
observación y análisis del cultivo en
terreno.
Balanzas de nutrientes para un
diagnóstico sistémico de la rotación
de cultivos.
Tres pruebas generalmente en uso:
Olsen: (valores de 0 a 50 generalmente) – extracción
sencilla y no muy fuerte; problemas en interpretación
debajo de pH de suelo = 5.
Bray 1 y 2: (valores de 0 a 100 o mas) Extracción mas
fuerte que el Olsen (Bray 2 es mas fuerte que Bray 1).
Tiene serios problemas en suelos neutros a alcalinos
(pH > 7)
Mehlich: (Valores de 0 a 100) Extracción mas fuerte,
sin problemas de interpretación debido a pH del suelo.
La prueba mas nueva, que permite analizar para varios
nutrientes (véase análisis de K)
Dos pruebas generalmente en uso:
Acetato de amonio y Mehlich 3, con
iguales rangos de suficiencia. Acetato
de amonio es mas tradicional y Mehlich
3 se introdujo para poder hacer un solo
análisis para componentes múltiples de
fertilidad (P, K, Mg, Ca, metales)
76
Appendix 6: Materials describing how much N, P, and K is removed from soils (kg/ha) by typical crops at different yield
levels. These materials were returned to farmers in communities where we did sampling for the nutrient budgeting
project. The yield units reflect the typical traditional yield measures of a ratio between yield and seed input. The
example for potato is shown here; graphs were also developed for maize, barley, wheat, oats, and fava beans.
Cantidad de nitrogeno (N) que sale de los terrenos por
cosecha de papa, segun rendimientos relativos
90
80
Kg/
ha
70
60
50
40
30
20
10
0
3:1-5:1
6:1-10:1
11:1 - 15:1
16:1-20:1
20:1-30:1
Ratio of yield:seed
77
Cantidad de fosforo (P) que sale de los terrenos por
14
12
10
8
Kg/
ha
6
4
2
0
3:1-5:1
6:1-10:1
11:1 - 15:1
16:1-20:1
20:1-30:1
78
Cantidad de Potasio (K) que sale de los terrenos por
140
120
100
Kg/
ha
80
60
40
20
0
3:1-5:1
6:1-10:1
11:1 - 15:1 16:1-20:1
20:1-30:1
79

Putting soil nutrient budgets into practice

Transcripción

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