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To ensure the productivity of smallholder maize production systems in Central America, increased attention must be paid to conserving soil and water resources. Various stakeholders from national agricultural research services (NARS), networks, non-governmental organizations (NGO’s) and research institutes seek to develop and target productivity enhancing resource conserving management practices such as crop residue retention systems. To support this process, a methodology with explicit spatial and temporal dimensions using the DSSAT crop simulation models was developed and applied to the case study of residue retention in maize production systems of Jalisco, Mexico. Systems where crop residues were partially or totally retained were compared with systems with no residue retention. Variables considered included crop production, soil surface runoff water, soil organic carbon and soil organic nitrogen content. Gridded climate surfaces and a range of soil profile scenarios formed the basis of the regional input. Three levels of nitrogen application (0, 150 and 235 kg ha-1) were selected to represent current agronomic practices. Simulation models for maize and fallow were run in sequence for 12 consecutive years. Results demonstrated that soil water holding capacity and topography affected benefits of residue retention much more than nitrogen management. Benefits to maize production from residue retention were found mainly in areas in the northeast of Jalisco and surrounding regions, where low precipitation limits maize production. Reductions in soil surface runoff due to residue retention were most evident in high rainfall areas in the central-east of Jalisco and surrounding regions. Although the effect of soil loss is not accounted for in DSSAT, areas of pronounced soil surface runoff reduction are hypothesized to be areas of productivity gain in the longer term. The assessment provided insight in the regional potential for and the soil and climate conditions under which successful introduction of residue retention in maize cropping systems in Jalisco may be achieved. In combination with socio-economic assessment studies and experimental work, the approach and its results contribute to targeted introduction of new production technologies. [Copyright &y& Elsevier]

The use and application of decision support systems (DDS) that consider variation in climate and soil conditions has expanded in recent years. Most of these DSS are based on crop simulation models that require daily weather data, so access to weather data, at single sites as well as large amount of sites that may cover a region, becomes a critical issue. In many agricultural regions, especially in developing countries, the density of meteorological stations is low, and reliable long-term continuous data are scarce. Researchers can use interpolated surfaces of weekly or monthly climate variables and generate daily weather from these. Various software tools, called ‘weather generators’, are available to automate this data generation process. The main objective of this study was to compare the performance of three weather generators, MARKSIM, SIMMETEO and WGEN, with observed daily weather data for one of the major maize growing regions in northwest Mexico. A second objective was to evaluate the impact of using different generators for creating daily weather data for the simulation of maize and bean growth at nine locations. No single generator was clearly superior. However, considering data requirements, the weather generator SIMMETEO is robust and can be recommended for (crop) modeling applications at single point locations as well as for applications that use interpolated summary weather data as input. The weather generator MARKSIM created a high inter-annual variability and long chains of wet days that are not found in observed data, but the generator has use for areas of poor distribution of weather stations or where monthly means are unavailable. The results from this study can be considered valid for the subtropical region from which the test locations were selected. For climates in different regions of the world, we suggest repeating the evaluation process following procedures similar to those used in this paper. [Copyright &y& Elsevier]

Velvet bean (Mucuna pruriens (L.) DC. cv.-group utilis) is widely promoted in tropical and sub-tropical regions as a green manure cover crop that can reduce weed growth and soil erosion and enhance soil fertility. To provide these benefits, the crop must attain rapid ground cover and develop substantial aboveground biomass. To assist biophysical targeting of the crop to environments that can provide adequate growth conditions, the CROPGRO model was adapted to simulate velvet bean growth and development. This paper evaluates the performance of the model for phenology, growth, senescence and N accumulation for multiple locations that represent a range of environmental and agronomic management scenarios. Vegetative development, as described by main stem leaf appearance rate, varied linearly with thermal time. Time to flowering showed departures from the linear photoperiod response used in the model. Additional research is required to determine whether the crop is influenced by factors besides photoperiod and air temperature, especially water and nutrient deficits. The linear response to photoperiod did, however, provide reasonable values for partitioning to vegetative, reproductive and senesced materials. Simulation of nitrogen concentration for various plant components matched observed data. Sensitivity analyses evaluating the ability of the crop to provide ground cover, intercept light and develop adequate growth for soil protection and weed suppression indicated that a mean temperature of over 22 °C and a soil moisture holding capacity of at least 100 mm are required. The CROPGRO model proved to be a reliable decision support tool for guiding analyses of velvet bean response to crop management and environmental conditions. Further research, however, is warranted to improve its predictive capability, especially for phenology. [Copyright &y& Elsevier]

Velvet bean (Mucuna pruriens (L.) DC cv. group utilis) is widely promoted as a GMCC for tropical regions. Reports of insufficient biomass production in certain environments and concerns over seed production, however, suggest a need for a more complete description of growth and development of velvet bean under different production scenarios and environments. Process-based simulation models offer the potential for facilitating an assessment of management strategies for different environments, soils and production systems. The objective of this study was to review the physiology of velvet bean and using the generic legume model CROPGRO, to provide a structured and quantitative framework for describing crop response to management and environment. Model coefficients used to describe growth and development of soybean (Glycine max (L.) Merr.) served as initial reference values. Information on velvet bean from published sources was then used to revise the functions and parameters of the model. Phenology, canopy development, growth and partitioning were calibrated for two velvet bean varieties using experimental data from three sites in Mexico. Compared to soybean, velvet bean has a much longer growth cycle, allowing a very large numbers of nodes to form. Velvet bean has larger, thinner leaves than soybean, resulting in more rapid leaf area development, and larger seeds, which affects germination, early season growth and pod development. A modification to CROPGRO to track senesced tissues was incorporated. Overall, the physiological processes underlying growth and development of velvet bean appear to be similar to other tropically adapted legumes. The new model, incorporated as part of the DSSAT, version 3.5 suite of crop simulation models, has potential for evaluating management strategies in specific environments and to identify potential regions for introduction of velvet bean as a green manure cover crop. [Copyright &y& Elsevier]