Modeling intercropping with cereals in smallholder agrosystems. From lessons learned in central Brazil to their application in the Peanut Basin in Senegal
A. BaldéLaure Tall DioufNiokhor BakhoumFrançois AffholderC.C. DauphinMarc CorbeelsNdjido Ardo KaneDominique MasséÉric Scopel
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In most areas of sub-humid tropics where the rainy period is too short to allow a system with a succession of crops, intercropping is an option to diversify culture and enhance agrosystems resiliency. However, interactions among associated crops (facilitation and/or competition) are complex, variable in time and will depend on the characteristics of each crop and the management of the whole system. Understanding and quantifying these complex interactions and their impacts on the agrosystem productivity require to consider temporal dimension. In fact, oneoff measures or entirely experimental approach cannot adequately answer these questions. However, crop modeling can complete experimental approaches by taking better account of changing interactions over time, allowing dynamic quantification of the flow of resources and their distribution. We will present an example of intercropping model with maize using STICS-CA model, adjusted calibrated and then evaluated for the Brazilian Cerrado system. We will then discuss on how to use a similar approach for millet-cowpea intercropping systems in the Senegalese peanut Basin.Keywords:
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Agroforestry, the intentional integration of trees with crops and/or livestock, can lead to multiple economic and ecological benefits compared to trees and crops/livestock grown separately. Field experimentation has been the primary approach to understanding the tree–crop interactions inherent in agroforestry. However, the number of field experiments has been limited by slow tree maturation and difficulty in obtaining consistent funding. Models have the potential to overcome these hurdles and rapidly advance understanding of agroforestry systems. Hi-sAFe is a mechanistic, biophysical model designed to explore the interactions within agroforestry systems that mix trees with crops. The model couples the pre-existing STICS crop model to a new tree model that includes several plasticity mechanisms responsive to tree–tree and tree–crop competition for light, water, and nitrogen. Monoculture crop and tree systems can also be simulated, enabling calculation of the land equivalent ratio. The model’s 3D and spatially explicit form is key for accurately representing many competition and facilitation processes. Hi-sAFe is a novel tool for exploring agroforestry designs (e.g., tree spacing, crop type, tree row orientation), management strategies (e.g., thinning, branch pruning, root pruning, fertilization, irrigation), and responses to environmental variation (e.g., latitude, climate change, soil depth, soil structure and fertility, fluctuating water table). By improving our understanding of the complex interactions within agroforestry systems, Hi-sAFe can ultimately facilitate adoption of agroforestry as a sustainable land-use practice.
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Despite the potential productivity benefits, intercrops are not widely used in modern, mechanised grain cropping systems such as those practised in Australia, due to the additional labour required and the added complexity of management (e.g. harvesting and handling of mixed grain). In this review we investigate this dilemma using a two-dimensional matrix to categorise and evaluate intercropping systems. The first dimension describes the acquisition and use of resources in complementary or facilitative interactions that can improve resource use efficiency. The outcome of this resource use is often quantified using the land equivalent ratio (LER). This is a measure of the relative land area required as monocultures to produce the same yields as achieved by an intercrop. Thus, an LER greater than 1 indicates a benefit of the intercrop mixture. The second dimension describes the benefits to a farming system arising not only from the productivity benefits relating to increased LER, but from other often unaccounted benefits related to improved product quality, rotational benefits within the cropping system, or to reduced business risks. We contend that a successful intercrop must have elements in both dimensions. To date most intercropping research has considered only one of these two possible dimensions. Intercrops in large, mechanised, rain-fed farming systems can comprise those of annual legumes with non-legume crops to improve N nutrition, or other species combinations that improve water use through hydraulic redistribution (the process whereby a deep-rooted plant extracts water from deep in the soil profile and releases a small proportion of this into the upper layers of the soil at night), or alter disease, pest or weed interactions. Combinations of varieties within cereal varieties were also considered. For our focus region in the southern Australian wheatbelt, we found few investigations that adequately dealt with the systems implications of intercrops on weeds, diseases and risk mitigation. The three main intercrop groups to date were (1) ‘peaola’ (canola-field pea intercrops) where 70% of intercrops (n = 34) had a 50% productivity increase over the monocultures, (2) cereal-grain legume intercrops (n = 22) where 64% showed increases in crop productivity compared with monocultures and (3) mixtures of cereal varieties (n = 113) where there was no evidence of a productivity increase compared with the single varieties. Our review suggests that intercropping may have a role in large rain-fed grain cropping systems, based on the biophysical benefits revealed in the studies to date. However, future research to develop viable intercrop options should identify and quantify the genotypic differences within crop species for adaptation to intercropping, the long-term rotational benefits associated with intercrops, and the yield variability and complexity-productivity trade-offs in order to provide more confidence for grower adoption. Farming systems models will be central to many of these investigations but are likely to require significant improvement to capture important processes in intercrops (e.g. competition for water, nutrients and light).
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There is actually a surge for reintroducing biodiversity in agricultural systems in order to reduce chemical inputs, suppress pests, and close biogeochemical cycles. The use of cover-crops is a promising way to reintroduce biodiversity into the fields. Cover-crops have the potential to decrease chemical use against weeds (by competition) and pests (by increasing in natural enemies). To decrease herbicide use, suitable plants must be able to grow in appropriate conditions, to do not compete the cultivated crop for nutrients or water, but should compete weeds for light and space. There is a trade-off between these objectives. Banana is a semi-perennial crop, each plant develops at its own rhythm leading to an unsynchronized plant population in three years; canopy and nitrogen demand of the crop follow this unsynchronized pattern. Banana cropping systems remain based on bare soil management and a large amount of herbicides is used. In tropical environment, the growth of weeds and cover-crop is complex because it is not constrained by seasons; a constant growth is possible due to relatively constant climate. In these conditions, variation in radiation due to canopy closure is one of the major drivers of their growth. We developed a model based method to assess the suitability of cover crop for a given cropping system context. This method first relies on early measurements of cover crop performed on the field. Then, we used a simulation model to contextualize the growth of cover crops and to assess their capacity to control weeds, to compete the cultivated plant, and to sustain on the long term under the shade of the main crop. This approach allows an early selection of cover crops that should be tested in real intercropping in the field. We present results of this evaluation for 11 species intended to banana intercropping. (Texte integral)
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Crop models are very suitable tools to investigate the potential of specific field management strategies to increase crop (water) productivity in a given environment. They are less time and resource consuming compared to field experiments and when well calibrated they allow for very efficient and extensive scenario analysis both for long-term historical climate data as for future climate scenarios. Moreover, crop models account for the fact that the effect of field management is strongly dependent on the complex interaction between the rainfall pattern, soil characteristics and cropping system of a particular location and time. Hence they contribute to the understanding of those interactions between environmental and management factors, and are able to provide information on efficient and sustainable field management strategies that is not affected by a specific experimental set up. This study presents a modelling approach to optimize crop (water) productivity in rainfed agriculture through improved field management, while at the same time improving our understanding of the interactions between management, soil, climate, and crop characteristics. AquaCrop, the crop water productivity model developed by the Food and Agriculture Organization (Hsiao et al., 2009; Raes et al., 2009; Steduto et al., 2009), is thereby used to explore a wide range of field management strategies for upgrading both crop yield (Y) and crop water productivity ( ) at farm scale. The potential of the presented modelling approach and the analysis of simulation results with special attention for the management - environment interactions, will be illustrated by an example scenario analysis.
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