The role of biological simulation models in farming systems research

doi:10.1016/0269-7475(88)90010-4

Agricultural Administration and Extension

Volume 29, Issue 2, 1988, Pages 111-122

Agricultural Adminis...

https://doi.org/10.1016/0269-7475(88)90010-4 Get rights and content

Abstract

The use of biological computer-based simulation models is considered with reference to Farming Systems Research. Such models have a role to play in the design of appropriate technology packages for poor farmers. The potential benefits for speeding the design process are such as to encourage the finding of solutions to, or the minimisation of the impact of, the severe conceptual problems that exist. Certain characteristics in the design of biological models can be enumerated, which may be expected to facilitate their use in such a process.

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Cited by (15)

Applications of crop/soil simulation models in tropical agricultural systems
2002, Advances in Agronomy
Crop simulation models have been used widely to describe systems and processes at the level of the genotype, the crop, the farming system, the region, and the global environment, but examples of how the use of such models has benefited poor people in developing countries are limited. There is, therefore, an urgent need to make the use of models in research more relevant to problems in the real world and to find effective means of disseminating the results from work using models to the potential beneficiaries.
To achieve this, we believe that there must be a shift in the thinking of crop/soil modelers toward making people more center stage and toward a more problem-solving approach. This means researchers must think of the real problems faced by ordinary people in developing countries and construct and apply their models to contribute to solving these problems. For this to be effective, modelers need to define clearly who are the end users of their models and to enter into dialogue with these people so that the final product is tailored to their needs.
There appear to be two opposite directions in which further crop modeling research can develop. On the one hand, in response to the rapidly expanding field of genomics, links between information at the gene level and performance at the phenotype level need to be established, and methodologies to do this must be developed. Such models will have the potential to contribute to enhancing the efficiency of crop improvement programs worldwide by providing more efficient ways of identifying and evaluating desirable characteristics for specific plant breeding goals. On the other hand, crop models need to be incorporated into higher order systems such as the whole farm, catchment, or region. Some progress has already been made in linking crop growth models with other physical process models to improve our understanding of how changes in agricultural systems influence overall environmental impacts. However, the role of people in these systems also needs to be made explicit so that the day-to-day decisions that they make to sustain and improve their livelihoods and the influence these decisions have on their environment and natural resource base can be taken into account.
National spatial crop yield simulation using GIS-based crop production model
2001, Ecological Modelling
Traditional decision support systems based on crop simulation models are normally site-specific. In policy formulation, however, spatial variability of crop production often need to be evaluated due to different soil conditions, weather conditions and agricultural practices within a target-region. To address the spatial variability, a spatial model ‘Spatial EPIC’ was developed based on a crop simulation model EPIC (Erosion Productivity Impact Calculator). Since site-specific crop simulation models require point-based or fine resolution data, it is necessary to feed the fine resolution data at each grid-cell in order to ‘spatialize’ crop simulation models. The authors proposed a method to generate fine resolution data from coarse resolution data, which are usually available at regional or national level. In addition, since the original EPIC crop management practices are static in nature, a dynamic adaptation loop is added to evaluate the impacts of agricultural practice changes over temporal scale. Validation of the spatial EPIC was conducted at different spatial scales, i.e. national scale (approx. 50 km cell-size) and regional scale (approx. 10 km cell-size) in India. Results showed that at both resolutions level crop yield varied significantly as a function of seasonal climatic variation, soil water holding characteristics and applied crop management strategies. Also, the study successfully demonstrated model applicability in evaluating an impact of climate changes over major cereal crops productivity at national level taking spatial variability into account.
Integrated crop-livestock simulation models for scenario analysis and impact assessment
2001, Agricultural Systems
Despite the fact that many smallholder farming systems in developing countries revolve around the interactions of crop and livestock enterprises, the modelling of these systems using combinations of detailed crop and livestock models is comparatively under-developed. A wide variety of separate crop and livestock models exists, but the nature of crop–livestock interactions, and their importance in smallholder farming systems, makes their integration difficult. Even where there is adequate understanding of the biophysical processes involved, integrated crop–livestock models may be constrained by lack of reliable data for calibration and validation. The construction from scratch of simulation models that meet the needs of one particular case is generally too costly to countenance. As for all modelling activity, the most efficient way to proceed depends on the nature of the systems under study and the precise questions that have to be addressed. We outline a framework for the integration of detailed biophysical crop and livestock simulation models. We highlight the need for minimum calibration and validation data sets, and conclude by listing various research problems that need attention. The application of robust and trustworthy crop–livestock models is critical for furthering the research agenda associated with animal agriculture in the tropics and subtropics.
Relationships between management intensity and structural and social variables in dairy and dual-purpose systems in Santa Cruz, Bolivia
2000, Agricultural Systems
The objective of this paper was to study the relationships between technological use and farm and farmer characteristics in dairy and dual-purpose farms in Santa Cruz, Bolivia. Quantitative and qualitative data related to farm dimensions, land use, pasture, nutrition, reproduction and health management and farm household social characteristics, access to information and technical assistance were collected from 319 farms in the main dairy-producing regions of Santa Cruz. Data were analysed by multiple correspondence analysis in order to establish relationships amongst management intensity and variables related to farm structure, productive orientation and the farmers’ social conditions and information exposure to technical aspects. A cluster analysis was then carried out to identify groups of farms with similar characteristics within the sample population. The analyses demonstrated clear relationships between management intensity, the farms’ dimensions and the farmers’ social conditions and access to information. Results are discussed in respect to the importance of farm characterisation for defining target groups and delivering research outputs and extension policies more effectively.
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Sustainability in agriculture involves inter-temporal tradeoffs in the use of natural resources. Analysis of sustainability necessarily involves the study of the dynamics of change in the stock of natural resources. To understand and analyse sustainability better it is important to be able to forecast the future consequences of current resource-use decisions. As the time frame involved may be long, and complex interactions among various parts of the farming systems need to be well understood, modelling is a good tool for studying such phenomena.
Following consideration of the role of various types of models of farming systems as substitutes for, or complementary to, real-world experimentation, it is contended that bio-economic simulation models are well-suited for studying sustainability. A powerful tool to evaluate sustainability implications of alternative management practices is provided by the framework of optimal control theory. Some illustrative applications of modelling are presented and possible problems in applying such models in the context of developing countries are presented.
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A hypothetical crop model capable of simulating a cereal-legume intercrop is outlined. Some of the uses to which it could be put are considered. The current status of two single-crop models of dry bean and maize is reviewed, and an attempt to join these together to enable simulation of combined bean-maize cropping systems is considered. Despite the desirability of a generic, portable intercrop model, its construction will require considerable research.

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The role of biological simulation models in farming systems research

Abstract

Agricultural Administration

Agricultural Administration

Agricultural Administration

Agricultural Administration and Extension

Agricultural Systems

Socioeconomic modelling of farming systems

Improving the economic impact of farming systems research

A farm growth model for an extensive pastoral production system

Australian Journal of Agricultural Economics

A sequential model of the farm firm growth process

American Journal of Agricultural Economics