Assessment of tradeoffs for biomass uses between livestock and soil cover at farm level
Naudin K1, Bruelle G2, Salgado P3, Penot E4, Scopel E1, Lubbers M5, de Ridder N5, Giller K E5
1
UPR Systèmes de Culture Annuels, CIRAD, F-34398 Montpellier, France ; [email protected]
2
FOFIFA, 110 Antananarivo, Madagascar
3
UMR SELMET, CIRAD, F-97410 Saint-Pierre, La Réunion, France
4
UMR Innovation, CIRAD, F-34398 Montpellier, France
5
Plant Production Systems, Wageningen University, Wageningen, The Netherlands
Keywords: biomass uses tradeoffs, soil cover, smallholder, linear programming
Introduction
One of the main constraints for implementation of CA in smallholder farming in Africa is maintaining an organic mulch as soil cover (Giller et
al., 2009). There are two principal problems. First it is difficult to produce sufficient biomass on poor soil without external fertilizer; and second,
there are competing uses for the biomass produced, especially for livestock feed. Integration of Conservation Agriculture (CA) and livestock has
rarely been reported, except in Brazil (Bolliger et al., 2006). In our study we focused on the production and use of biomass within CA cropping
systems. The biomass can be used both as forage for cattle or as mulch for CA. The ecological functions of mulch are physical and chemical.
The physical functions relating to soil cover include i) weed control (Teasdale and Mohler, 2000), ii) erosion control (Smets et al., 2008), and
iii) improvement of the crop water balance due to promotion of infiltration and reduction of evaporation losses (Scopel et al., 2004). The
chemical effects include provision of nutrients for plant growth and provision of chemical buffering for nutrient retention and against soil
acidity, due to inputs of organic matter and nutrients (Maltas et al., 2009; Neto et al., 2010). In this study we focus on the short-term effects of
mulch, which are more related to soil cover than to chemical effects. Short-term benefits including weed control and an improved crop water
balance. We developed an optimization model, using multiple goals linear programming, of a representative medium-sized Malagasy farm. This
model was named GANESH (Goals oriented Approach to use No till for better Economic and environmental sustainability for SmallHolders).
Using this model we test the following hypotheses: i) if we do not constrain the choice of cropping systems (CA or not, minimum percent of soil
cover) and offer an unlimited market for milk, with access to an improved dairy breed and fixed prices for milk, farmers will choose to use as
much biomass as they can to feed their animals. Thus the remaining biomass quantity will not be sufficient to implement CA; ii) Conversely, if
the market for milk is limited or prices low, only part of the biomass produced by CA systems can be used to feed cattle with a small impact on
soil cover while allowing increased animal production.
Materials and Methods
The GANESH model is written in GAMS (22.5.148) with an Excel 2003 interface. It includes 24 variables and 51 equations. The model
structure can be divided into three main components: i) the crops, ii) the animal herd, iii) the farm (Figure 1). In the crop component we made a
distinction between four kinds of fields: hillside fields (tanety), alluvial soils (baiboho), poor water control paddy field, and irrigated paddy
fields. For each kind of field a list of possible crops for the growing season and the off-season was established. Each crop was characterized by
an input-output table. Inputs include total labour and chemicals (e.g.: fertiliser, herbicides and insecticides). Outputs are grain and above-ground
biomass and its quantity and quality (protein, energy). The biomass produced can be used to feed cattle. Part of the crop biomass produced
during one year can be used to feed cattle and the remainder used as mulch. The relation between mulch quantity and soil cover as described by
Gregory (1982) was locally calibrated (Naudin et al., in press). The livestock component comprised four kinds of animals: zebu male, zebu
female, dairy cow improved breed, and dairy cow local breed. The inputs for these production activities were the labour requirement and forage
parameters (protein, energy, quantity of dry matter ingested). The outputs were milk, manure, heifers and calves sold, and draught power. The
farm component comprised the number and type of people (for estimating household food demand and workforce available), and the number and
area of fields. Beside these three components, external factors influencing the functioning of the farming system were included in the model:
milk price, quantity of milk marketed, and price of hired labour.
CROP
External factors
Poor water control paddy
field
Season
Off
CT rice
season
CA rice
Vicia
Fallow
Hillsides
Season
Brachiaria
CT groundnut
CT rice
CT cassava
CT maize+dolichos
CT grounut+stylo
CT cassava+brachiaria
CT maize
CA rice
CA maize+dolichos
CA groundnut+stylo
Alluvial
soil
Stylosanthes
Season
Off
CT rice
season
CT maize
Vicia
CT
Fallow
maize+dolichos
Dolichos
CA rice
Irrigated
paddy field
CA
maize+dolichos
Season
CT rice
Milk price
Max. quantity
marketable
of
FAR
M
Nb of people
Needs
Work Manure
Chemical inputs
Outputs
Grain
Abovegroun
d biomass
Soil cover
Forage
available
Nb of fields for each
kind
milk
Needs
Work force
Self consumption needs
Optimisation
Final results
Total income
Farm plan
Margin from animal Margin from crops
Exported biomass
Soil cover
External labor
Constraint
% of soil cover at crops
seeding
LIVESTOCK
Needs
Work
Zebu male
Forage (energy, protein,Zebu female
biomass)
Dairy cow improved
Outputs
breed
Milk
Solded heifer and calvesDairy cow local breed
Solded draught power
Manure
Figure 1. Conceptual scheme of the GANESH model. Other input variables are: milk prices, maximum quantity of milk marketable, inputs
prices, hired labour prices. Results are: total farm income, economic margin from animal products, quantity of hired labour, detailed farm plan
over three years for each field included in the model. The main constraint, which determines the quantity of biomass available for cattle feeding
and mulch, is the percent of soil cover. This value of minimum percent of soil cover is parameterised by the user to draw, by iteration, the
relation presented in Figure 2.
Figure 2. Proposed theoretical relationship between soil cover of a field, or group of field, and economic margin from livestock (milk, meat,
young animals) coming from biomass uses as forage. The relation is non -linear as the relationship between soil cover and biomass quantity on
soil is non-linear. Thus when the biomass produced is sufficient to begin with biomass exportation has only a small influence on soil cover but
can allow increased animal production.
Results and Discussion
The model is currently being validated. At present we do not have complete output from the GANESH model and thus we present probable
results. Concerning the hypothesis i and ii (see introduction), we expected a theoretical relation between soil cover and economic margin from
livestock as shown in Figure 2. The first hypothesis is that in a context of high pressure on plant residues due to dairy production, the farmer
tends to favour animal feeding at the expense of soil cover. Supporting this hypothesis and model output is the weak CA adoption in the
Vakinankaratra region. In this region, almost none of the CA systems were adopted by farmers, because it is more profitable to give crops and
cover crop residues to dairy cows than to leave it as soil cover (Penot and Duba, 2011). The second hypothesis is that part of the biomass from
CA can be used as forage, improving animal production, with small impact on soil cover. This hypothesis is more difficult to observe in real
conditions; especially when the biomass exportation threshold has a strong impact on soil cover. Thus modelling could be a useful tool to
investigate this relation and threshold. However, the CA adoption rate in the Lake Alaotra region seems to be partially due to a low pressure
from cattle feeding. Biomass production is higher in CA systems due to better resource efficiency in time (off-season cover crops) or in space
(crop-cover crop associations) (Balde et al., 2011). This additional biomass can be shared between livestock and soil cover. At field level, among
the potential effects of soil cover, only weed control was taken into account as it is a short-term effect. Below a threshold, biomass exportation
starts to have a negative impact on margins and/or returns to labour of the cropping system because a low soil cover does not provide effective
weed control. Thresholds of biomass exportation, below which the gains from animal production compensate for the loss from crop production,
vary depending on the biomass production, potential animal production and the market prices of animal products. Relationships between crop
and animal production for labour, income, field allocation, are required to model these trade-offs at farm level. Indeed the objective is to propose
a choice of various cropping systems management (with more or less biomass export) coping with farming goals as a whole and not only to
focus on crop or livestock components.
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