Designing innovative combinations of techniques to improve the sustainability of a cropping system is a major challenge in many regions of the world. The available techniques are often added together, and assessed for yield only, rather than combined in an integrated approach. We than developed here a methodology to design and assess a sustainable crop management system adapted to a specific set of constraints. Based on the prototyping approach, this methodology takes advantage of expert knowledge on cotton cropping techniques such as no-till, cover crop, varieties and growth regulator, with innovative potential but which are not yet properly simulated by actual crop models. It involves four successive steps: (1) identification of the local sets of constraints to crop production, and selection of relevant criteria for sustainability assessment, (2) elaboration of a cropping system prototype and its assessment indicators adapted to a target set of constraints, (3) on-station assessment and adjustment of the prototype, and (4) on-farm evaluation and adjustment of the prototype. We describe here the methodology, and how its first three steps were implemented to build and test a prototype for late-planted cotton with low input availability in West Africa. A new cropping system was designed, which included new genotypes, increased plant stand, lower rates of fertilisers and the use of herbicides and growth regulators. Fourteen indicators were selected to assess the economic, environmental and social performance of the prototype. The prototype was then tested in Mali, Cameroon, and Benin in 2002 and 2003. Our findings show that this prototype improved farmers' income by about +35% in 2002 and +20% in 2003, and increased labour productivity by about +5 to +20%. It achieved a satisfactory environmental performance, similar to the control, with positive mineral balances. The prototype still requires extra labour, skill and money to implement, and therefore requires further adjustment. sustainable cotton management system / west Africa / prototyping / multi-criteria evaluation
Smallholder farmers in sub‐Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low‐input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi‐arid Rwanda, hot subhumid Ghana and hot semi‐arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in‐season soil water content from 2‐year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low‐input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.
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