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Models that represent biophysical processes in hydrology, ecology and agricultural systems, when applied at specific locations, can make estimates with significant errors if meteorological input data are not representative of the sites. This is particularly important where the estimates from the models are used for decision support, strategic planning and policy development, due to the impacts of introduced uncertainty. This paper investigates the impacts of meteorological data sources on a cropping systems simulation model’s estimate of crop yield, and quantifies the uncertainty that arises when site-specific weather data are not available. In the UK, as elsewhere, many meteorological stations record precipitation and air temperature, but very few also record solar radiation, a key driving input data set. The impacts of using on-site observed precipitation and temperature with estimated solar radiation, and off-site entirely observed meteorological data was tested on the model’s yield estimates. This gave two scenarios: on-site observed versus partially modelled data; and on-site observed versus substitute data from neighbouring sites, for 24 meteorological stations in the UK. The analysis indicates that neighbouring meteorological stations can often be an inappropriate source of data. Of the 24 stations used, only 32% of the nearest neighbours were able to provide the best substitute off-site data. On-site modelled data provided better results than observed off-site data. The results demonstrate that the range of alternative data sources tested are capable of having both acceptable and unacceptable impacts on model performance across a range of assessment metrics, i.e. on-site data sources each produced yield over- or under-estimate errors greater than 2 t ha −1 . A large amount of uncertainty can be introduced to the model estimates due to the data source. Therefore, the applications of models that represent biophysical process where meteorological data are required, need to include the quantification of input data errors and estimate of the uncertainty that imperfect data will introduce.

This paper reports the outcomes of a deliberative workshop comparing land-use plans proposed by land-manager or domain experts with those derived using a computer-based decision support system (DSS). The DSS integrates four main components, a geographic information system, land-use systems simulation models, impact assessments and land-use planning tools. The land-use planning tools draw on the other components to generate and evaluate alternative patterns of land use and management. Since the land-use planning tools are based on multi-objective genetic algorithms (mGAs) it is possible to generate a range of alternative plans that define the structure of the trade-off between the objectives. The workshop tasked the delegates with specifying land-use plans that achieved the best compromise between two objectives known to be non-commensurable and conflicting. The nature of the best compromise was dependent on their individual perspectives. The delegates proposed allocations both as individuals and in researcher-facilitated sub-groups. The mGA allocations were then compared with those derived by delegates and were found to be broadly similar in performance. Differences in the range of allocations considered feasible were explained by the hard and soft constraints on allocations agreed between the delegates and articulated within the workshop process. The hypothesis that part of the difference in performance between the mGA and delegate allocations was due to the delegates blocking together fields with the same land use for convenience of management was proved. The analysis of the group allocations revealed that the decision-making process had failed to improve on the individual allocations. From these results it was concluded that there was a potential role for mGA based land-use planning tools in researching into, and deliberating on, the possible impacts of policy or other factors affecting land-use systems. It was further concluded that the tools should not be used in isolation since there was the need for stake-holder inputs to adequately define the range of feasible and practical land-use plans.

The development of cropping systems simulation capabilities world-wide combined with easy access to powerful computing has resulted in a plethora of agricultural models and consequently, model applications. Nonetheless, the scientific credibility of such applications and their relevance to farming practice is still being questioned. Our objective in this paper is to highlight some of the model applications from which benefits for farmers were or could be obtained via changed agricultural practice or policy. Changed on-farm practice due to the direct contribution of modelling, while keenly sought after, may in some cases be less achievable than a contribution via agricultural policies. This paper is intended to give some guidance for future model applications. It is not a comprehensive review of model applications, nor is it intended to discuss modelling in the context of social science or extension policy. Rather, we take snapshots around the globe to 'take stock' and to demonstrate that well-defined financial and environmental benefits can be obtained on-farm from the use of models. We highlight the importance of 'relevance' and hence the importance of true partnerships between all stakeholders (farmer, scientists, advisers) for the successful development and adoption of simulation approaches. Specifically, we address some key points that are essential for successful model applications such as: (1) issues to be addressed must be neither trivial nor obvious; (2) a modelling approach must reduce complexity rather than proliferate choices in order to aid the decision-making process (3) the cropping systems must be sufficiently flexible to allow management interventions based on insights gained from models. The pro and cons of normative approaches (e.g. decision support software that can reach a wide audience quickly but are often poorly contextualized for any individual client) versus model applications within the context of an individual client's situation will also be discussed. We suggest that a tandem approach is necessary whereby the latter is used in the early stages of model application for confidence building amongst client groups. This paper focuses on five specific regions that differ fundamentally in terms of environment and socio-economic structure and hence in their requirements for successful model applications. Specifically, we will give examples from Australia and South America (high climatic variability, large areas, low input, technologically advanced); Africa (high climatic variability, small areas, low input, subsistence agriculture); India (high climatic variability, small areas, medium level inputs, technologically progressing; and Europe (relatively low climatic variability, small areas, high input, technologically advanced). The contrast between Australia and Europe will further demonstrate how successful model applications are strongly influenced by the policy framework within which producers operate. We suggest that this might eventually lead to better adoption of fully integrated systems approaches and result in the development of resilient farming systems that are in tune with current climatic conditions and are adaptable to biophysical and socioeconomic variability and change. (C) 2001 Elsevier Science Ltd. All rights reserved.

The need for tools to evaluate cropping systems and their impact on production and the environment is increasing. Field experiments, which are usually site specific, require considerable resources and time to test hypotheses or management options. Computer models for cropping systems simulation are an alternative, but they require calibration and validation before applications can be developed for a given region. The ability of CropSyst, a cropping systems simulation model, to simulate spring and winter wheat yields and evapotranspiration in a wheat-fallow region of eastern Washington for different tillage and residue management practices was evaluated. Validation of the model was accomplished by (a) providing initial conditions prior to planting for each cropping season of a 6-year wheat-fallow rotation and (b) providing initial conditions at the beginning of the rotation without reinitializing the model in later years. The statistical structure (mean and SD) of simulations and field data were similar. The root mean square errors fluctuated from 7 to 14% of the observed mean for grain yield and from 5 to 9% for evapotranspiration, depending on the type of evaluation, and was 17% for soil moisture profile in the case of continuous 6-year rotation simulation without reinitialization. The Wilmott index of agreement fluctuated from 0.92 to 0.97 (an index of 1.0 indicating perfect agreement), with all values but one equal or better than 0.94. Similar water production functions were obtained from simulation and field data. Based on this validation, Crop'Syst appears promising as a tool to analyze the effectiveness of management practices for water conservation in a wheat-fallow cropping region.

Animal traction often forms an important part of smallholder farming systems. To model it as an essential link between the cropping and livestock subsystems, concepts from agricultural engineering, soil science, agronomy and animal production must be integrated. The conceptual model described in this paper includes traction requirements, traction supply, nutrient requirements for traction, and influences on other production functions. A sensitivity analysis singled out the most important input variables; working time is influenced most by working width, parcel area, and speed; the energy costs of traction are influenced most by working width and parcel area. Validation of the model with data from Pabna District, Bangladesh, shows that the model properly simulates the actual situation. Actual working times and live weight changes are closely approximated. Energy requirements for traction amount to 0.75–1.01 MJ net energy/h.

This paper shows how systems-simulation can be applied to answer agricultural development type questions. A computer simulation model of the Venezuelan cattle industry is presented, some of the validation efforts are discussed, and results from a number of computer runs are shown in order to illustrate how alternative programmes might affect the transition of the traditional cattle population to a more modern and productive population. Different programmes are shown to accomplish some objectives better than others. No optimisation over all possible development objectives was attempted, as this would depend upon the relative weights attached to the objectives by the decision-makers. It has been the authors' experience that modelling of biological and economic processes provides an important link between observations about the real world (raw data) and decision-makers in an information system designed to provide useful information. The building and operation of the computer model of the Venezuelan cattle population illustrates how a systems-simulation approach can be used in policy formulation.

Tsai et al . (1987) developed a combined network optimization—simulation model for optimal sequencing of multiple cropping systems. Essentially, simulations were used to generate a deterministic activity network. Then an optimization technique (K Longest Path algorithm) was applied to solve optimal sequences of multiple cropping. Using north Florida as a study region, the model was utilized to investigate optimal multiple cropping sequences in an irrigated or non-irrigated field. The results indicated that, for a non-irrigated farm, winter wheat followed by either soybean, maize or peanut was the most profitable cropping rotation in a multiple cropping sequence. Especially favorable was the double cropping of wheat—peanut. For an irrigated farm, a peanut crop was found to be prominent. In the case where peanut was not considered in the rotation, inclusion of irrigated wheat—maize cropping could not be recommended as a profitable multiple cropping system. Instead, double cropping of maize—soybean was the main scheme under irrigation with the possible substitution of a wheat—soybean crop sequence. To obtain higher, stable net returns, a north Florida farmer with no irrigation capability should plan his production system according to multiple cropping sequences SQ2 or SQ4 in the study. Use of these results for real-time decision making requires that the optimization be evaluated to select each new crop using current farm status and future expected weather and market conditions.

The complex nature of plant disease epidemic makes them well suited to systems simulation studies. This paper reports the initial work performed by the authors in developing a model of cereal rust diseases which is sufficiently flexible to be used over a wide range of host/rust race combinations and climatic conditions. The model is intended for use in: (a) investigating the relationships between components of an epidemic; (b) studying the progress of an epidemic under specified field conditions and (c) predicting crop yield loss as a result of a given epidemic. The general structure of the model is described and then a factorial simulation experiment is discussed. This experiment illustrates the use of the model in studying the effect of four major parameters on the development of a theoretical cereal rust disease epidemic. Results from the experiment are discussed in relation to published empirical data.