Learning from biophysical heterogeneity: inductive use of case studies for maize cropping systems in Central America

Global society has become conscious that efforts towards securing food production will only be successful if agricultural production increases are obtained through mechanisms that ensure active regeneration of the natural resource base. Production options should be targeted in the sense of that their suitability to improve agricultural production and maintain natural resources is evaluated prior to their introduction. Biophysical targeting evaluates production options as a function of the spatial and temporal variability of climate conditions, in interaction with soil, crop characteristics and agronomic management strategies. This thesis contributes to the development of a system-based methodology for biophysical targeting. Cropping system simulation and weather generator tools are interfaced to geographical information systems. Inductive use of two case studies - a green manure cover crop and reduced tillage with residue management - helped to develop the methodology. Insight is gained into the regional potential for and the soil and climate conditions under which successful introduction of these production options may be achieved. The resulting information supports regional stakeholders involved in agriculture in their analysis and discussion, negotiation and decision-making concerning where to implement production systems. This process can improve the supply of appropriate agricultural production practices that enhance production and conserve soil and water resources

Comparison of three weather generators for crop modeling: a case study for subtropical environments

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

Kriging and thin plate splines for mapping climate variables

Four forms of kriging and three forms of thin plate splines are discussed in this paper to predict monthly maximum temperature and monthly mean precipitation in Jalisco State of Mexico. Results show that techniques using elevation as additional information improve the prediction results considerably. From these techniques, trivariate regression-kriging and trivariate thin plate splines performed best. The results of monthly maximum temperature are much clearer than the results of monthly mean precipitation, because the modeling of precipitation is more troublesome due to higher variability in the data and their non-Gaussian character

Kriging and thin plate splines for mapping climate variables

Four forms of kriging and three forms of thin plate splines are discussed in this paper to predict monthly maximum temperature and monthly mean precipitation in Jalisco State of Mexico. Results show that techniques using elevation as additional information improve the prediction results considerably. From these techniques, trivariate regression-kriging and trivariate thin plate splines performed best. The results of monthly maximum temperature are much clearer than the results of monthly mean precipitation, because the modeling of precipitation is more troublesome due to higher variability in the data and their non-Gaussian character

Adaptation of the CROPGRO growth model to velvet bean (Mucuna pruriens) : I. Model development

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