Integrated crop–livestock simulation models for scenario analysis and impact assessment

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.     

Livestock water productivity in mixed crop–livestock farming systems of the Blue Nile basin: Assessing variability and prospects for improvement

Water scarcity is a major factor limiting food production. Improving Livestock Water Productivity (LWP) is one of the approaches to address those problems. LWP is defined as the ratio of livestock’s beneficial outputs and services to water depleted in their production. Increasing LWP can help achieve more production per unit of water depleted. In this study we assess the spatial variability of LWP in three farming systems (rice-based, millet-based and barley-based) of the Gumera watershed in the highlands of the Blue Nile basin, Ethiopia. We collected data on land use, livestock management and climatic variables using focused group discussions, field observation and secondary data. We estimated the water depleted by evapotranspiration (ET) and beneficial animal products and services and then calculated LWP. Our results suggest that LWP is comparable with crop water productivity at watershed scales. Variability of LWP across farming systems of the Gumera watershed was apparent and this can be explained by farmers’ livelihood strategies and prevailing biophysical conditions. In view of the results there are opportunities to improve LWP: improved feed sourcing, enhancing livestock productivity and multiple livestock use strategies can help make animal production more water productive. Attempts to improve agricultural water productivity, at system scale, must recognize differences among systems and optimize resources use by system components.     

Communicating complexity: Integrated assessment of trade-offs concerning soil fertility management within African farming systems to support innovation and development

African farming systems are highly heterogeneous: between agroecological and socioeconomic environments, in the wide variability in farmers’ resource endowments and in farm management. This means that single solutions (or ‘silver bullets’) for improving farm productivity do not exist. Yet to date few approaches to understand constraints and explore options for change have tackled the bewildering complexity of African farming systems. In this paper we describe the Nutrient Use in Animal and Cropping systems - Efficiencies and Scales (NUANCES) framework. NUANCES offers a structured approach to unravel and understand the complexity of African farming to identify what we term ‘best-fit’ technologies - technologies targeted to specific types of farmers and to specific niches within their farms. The NUANCES framework is not ‘just another computer model’! We combine the tools of systems analysis and experimentation, detailed field observations and surveys, incorporate expert knowledge (local knowledge and results of research), generate databases, and apply simulation models to analyse performance of farms, and the impacts of introducing new technologies. We have analysed and described complexity of farming systems, their external drivers and some of the mechanisms that result in (in)efficient use of scarce resources. Studying sites across sub-Saharan Africa has provided insights in the trajectories of change in farming systems in response to population growth, economic conditions and climate variability (cycles of drier and wetter years) and climate change. In regions where human population is dense and land scarce, farm typologies have proven useful to target technologies between farmers of different production objectives and resource endowment (notably in terms of land, labour and capacity for investment). In such regions we could categorise types of fields on the basis of their responsiveness to soil improving technologies along soil fertility gradients, relying on local indicators to differentiate those that may be managed through ‘maintenance fertilization’ from fields that are highly-responsive to fertilizers and fields that require rehabilitation before yields can improved. Where human population pressure on the land is less intense, farm and field types are harder to discern, without clear patterns. Nutrient cycling through livestock is in principle not efficient for increasing food production due to increased nutrient losses, but is attractive for farmers due to the multiple functions of livestock. We identified trade-offs between income generation, soil conservation and community agreements through optimising concurrent objectives at farm and village levels. These examples show that future analyses must focus at farm and farming system level and not at the level of individual fields to achieve appropriate targeting of technologies - both between locations and between farms at any given location. The approach for integrated assessment described here can be used ex ante to explore the potential of best-fit technologies and the ways they can be best combined at farm level. The dynamic and integrated nature of the framework allows the impact of changes in external drivers such as climate change or development policy to be analysed. Fundamental questions for integrated analysis relate to the site-specific knowledge and the simplification of processes required to integrate and move from one level to the next.     

Assessing the performance of a nucleus estate and smallholder scheme for oil palm production in West Sumatra: a stochastic frontier analysis

In this paper, we study the performance of smallholders in a nucleus estate and smallholder (NES) scheme in oil palm production schemein West Sumatra by measuring their technical efficiency using a stochastic frontier production function. Our results indicate a mean technical efficiency of 66%, which is below what we would have expected given the uniformity of the climate, soils and plantation construction among the sample farmers. The use of progressive farmers as a means of disseminating extension advice does not appear to have been successful, and more rigorous farmer selection procedures need to be put in place for similar schemes and for general agricultural extension in future. No clear relationship was established between technical efficiency and the use of female labour, suggesting there is no need to target extension services specifically at female labourers in the household. Finally, education was found to have an unexpectedly negative impact on technical efficiency, indicating that farmers with primary education may be more important than those with secondary and tertiary education as targets of development schemes and extension programs entailing non-formal education.     

A systems approach for the estimation of the effects of land consolidation projects (LCPs): a model and its application

Land consolidation projects (LCPs) are costly rural development actions that are often questioned. Integrated LCPs are geographically confined Land Rural Development Actions and their ex ante evaluation involves interdisciplinary research, in order to predict changes in farmers' behaviour, patterns of land use and in crops and technologies used. A model that incorporates methods for the evaluation of the performance of the agricultural system before and after the transformations proposed in the project is presented. It is argued that the systems approach is the appropriate method to integrate each particular change and evaluate the global impact of every action included in the project. The model was applied to the Valença-LCP in 1989, before project execution (Coelho, 1992 Análise de Projectos de Emparcelamento Rural. O caso de Valença do Minho. PhD thesis, UTL, ISA, Lisboa.). It evaluates each effect of the project (land, irrigation and drainage and road reconstruction) on a technical and social basis and estimates its economic impacts. The observed results used to evaluate model performance were obtained in 1995, after project implementation. The results from the comparison of model predictions (ex ante evaluation) with observations after LCP implementation (ex post evaluation) suggest that a multidisciplinary approach such as the one proposed here, supported by robust models, can be used as a reliable basis for the evaluation and decision-making process of LCPs.     

Cropping systems and crop residue management in the Trans-Gangetic Plains: Issues and challenges for conservation agriculture from village surveys

Conservation agriculture practices are being advocated to help sustain crop productivity gains and secure environmental sustainability in the Trans-Gangetic Plains, India’s Green Revolution heartland. The paper illustrates the use of village surveys as a quasi-quantitative system analysis tool to derive implications for agricultural research and development. Drawing from village surveys in 170 communities, the paper assesses current crop residue management practices in Punjab and Haryana’s rice-wheat, basmati-wheat and non-rice-wheat cropping systems. The prevalence of wheat as the winter crop implies an intensive collection, trading and use of wheat straw as basal feed for dairy livestock; which contrasts with the diverse crop residue management of the monsoon crops. The increased use of combine harvesters has spurred the rapid advent of mechanical wheat straw reapers whereas the bulk of combine harvested rice straw is burned in situ. Present crop residue management practices are largely incompatible with year-round mulch retention despite significant biomass production. The research and development community faces the challenge of evening out straw use and management over seasons to ensure at least partial residue retention if its calls for conservation agriculture in this important sub-region are to succeed. The paper also reiterates the worrying decline of groundwater tables associated with the rice-wheat system.     

InfoCrop: A dynamic simulation model for the assessment of crop yields,losses due to pests,and environmental impact of agro-ecosystems in tropical environments. II. Performance of the model

InfoCrop, a generic crop model, simulates the effects of weather, soils, agronomic management (planting, nitrogen, residues and irrigation) and major pests on crop growth, yield, soil carbon, nitrogen and water, and greenhouse gas emissions. This paper presents results of its evaluation in terms of its validation for rice and wheat crops in contrasting agro-environments of tropics, sensitivity to the key inputs, and also illustrates two typical applications of the model. Eleven diverse field experiments, having treatments of location, seasons, varieties, nitrogen management, organic matter, irrigation, and multiple pest incidences were used for validation. Grain yields in these experiments varied from 2.8 to 7.2 ton ha⁻¹ in rice and from 3.6 to 5.5 ton ha⁻¹ in wheat. The results indicated that the model was generally able to explain the differences in biomass, grain yield, emissions of carbon dioxide, methane and nitrous oxides, and long-term trends in soil organic carbon, in diverse agro-environments. The losses in dry matter and grain yield due to different pests and their populations were also explained satisfactorily. There were some discrepancies in the simulated emission of these gases during first few days after sowing/transplanting possibly because of the absence of tillage effects in the model. The sensitivity of the model to change in ambient temperature, crop duration and pest incidence was similar to the available field knowledge. The application of the model to quantify multiple pests damage through iso-loss curves is demonstrated. Another application illustrated is the use of InfoCrop for analyzing the trade-offs between increasing crop production, agronomic management strategies, and their global warming potential.     

FSSIM, a bio-economic farm model for simulating the response of EU farming systems to agricultural and environmental policies

The disciplinary nature of most existing farm models as well as the issue specific orientation of most of the studies in agricultural systems research are main reasons for the limited use and re-use of bio-economic modelling for the ex-ante integrated assessment of policy decisions. The objective of this article is to present a bio-economic farm model that is generic and re-usable for different bio-physical and socio-economic contexts, facilitating the linking of micro and macro analysis or to provide detailed analysis of farming systems in a specific region. Model use is illustrated in this paper with an analysis of the impacts of the CAP reform of 2003 for arable and livestock farms in a context of market liberalization. Results from the application of the model to representative farms in Flevoland (the Netherlands) and Midi-Pyrenees (France) shows that CAP reform 2003 under market liberalization will cause substantial substitution of root crops and durum wheat by vegetables and oilseed crops. Much of the set-aside area will be put into production intensifying the existing farming systems. Abolishment of the milk quota system will cause an increase of the average herd size. The average total gross margin of farm types in Flevoland decreases while the average total gross margin of farms in Midi-Pyrenees increases. The results show that the model can simulate arable and livestock farm types of two regions different from a bio-physical and socio-economic point of view and it can deal with a variety of policy instruments. The examples show that the model can be (re-)used as a basis for future research and as a comprehensive tool for future policy analysis.     

Adapting PILOTE model for water and yield management under direct seeding system: The case of corn and durum wheat in a Mediterranean context

Crop models are useful tools for integrating knowledge of biophysical processes governing the plant-soil-atmosphere system. But few of them are easily usable for water and yield management especially under specific cropping systems such as direct seeding. Direct seeding into mulch (DSM) is an alternative for conventional tillage (CT). DSM modifies soil properties and creates a different microclimate from CT. So that, we should consequently consider these new conditions to develop or to adapt models. The aim of this study was to calibrate and validate the PILOTE [Mailhol, J.C., Olufayo, A.A., Ruelle, P., 1997. Sorghum and sunflower evapotranspiration and yield from simulated leaf area index. Agric. Water Manag. 35, 167-182; Mailhol, J.C., Zaïri A., Slatni A., Ben Nouma, B., El Amami, H., 2004. Analysis of irrigation systems and irrigation strategies for durum wheat in Tunisia. Agric. Water Manag. 70, 19-37], an operative crop model based on the leaf area index (LAI) simulation, for corn and durum wheat in both DSM and CT systems in Mediterranean climate. In DSM case, simple model modifications were proposed. This modified PILOTE version accounts for mulch and its impact on soil evaporation. In addition root progression was modified to account for lower soil temperatures in DSM for winter crops. PILOTE was calibrated and validated against field data collected from a 7-year trial at the experimental station of Lavalette (SE of France). Results indicated that PILOTE satisfactorily simulates LAI, soil water reserve (SWR), grain yield, and dry matter yield in both systems. The minimum coefficient of efficiency for SWR was 0.90. This new version of PILOTE can thus be used to manage water and yield under CT and DSM systems in Mediterranean climate.     

InfoCrop: A dynamic simulation model for the assessment of crop yields,losses due to pests,and environmental impact of agro-ecosystems in tropical environments. I. Model description

The problems of agriculture in many tropical countries are gradually becoming more intense due to increasing food demand led by population growth, stagnation in farm productivity, mounting yield losses due to multiple pests, increasing vulnerability to global environmental changes and the need to reduce emission of greenhouse gases. Tools and techniques are needed to assist in developing strategies that can lead to higher food production, prevent crop production losses, and ensure minimal greenhouse gas emissions while maintaining soil fertility. Several dynamic models have been developed in recent past but most of these are generally strong either in soils and crops, or in greenhouse gases (GHG) emissions. Pest induced yield losses, a critical issue in the tropics, is not addressed in most models. InfoCrop, a generic dynamic crop model, has been developed to meet these specific requirements. It provides integrated assessment of the effect of weather, variety, pests, soil and management practices on crop growth and yield, as well as on soil nitrogen and organic carbon dynamics in aerobic as well as anaerobic conditions, and greenhouse gas emissions. The model considers the key processes related to crop growth, effects of water deficit, flooding, nitrogen management, temperature and frost stresses, crop–pest interactions, soil water and nitrogen balance and (soil) organic carbon dynamics. Its general structure relating to basic crop growth and yield is largely based on several earlier models, especially SUCROS series, and is written in Fortran Simulation Environment (FSE) programming language. The model has been validated for dry matter and grain yields of several annual crops, losses due to multiple diseases and pests, and emissions of carbon dioxide, methane and nitrous oxide in a variety of agro-environments. To increase the applications of model in research and development, an extremely simple menu driven version of InfoCrop has also been developed. The users of this version do not need any background in programming.     

Designing crop technology for a future climate: An example using response surface methodology and the CERES-Wheat model

Future crop production will be adapted to climate change by implementing alternative management practices and developing new genotypes that are adapted to future climatic conditions. It is difficult to predict what new agronomic technologies will be necessary for crop production under future climatic conditions. The purpose of this work was to develop an approach useful in identifying crop technologies for future climatic conditions. As an example of the approach, we used response surface methodology (RSM) in connection with the CERES-Wheat model and the HADCM2 climate simulation model to identify optimal configurations of plant traits and management practices that maximize yield of winter wheat in high CO₂ environments. The simulations were conducted for three Nebraska locations differing in altitude and rainfall (Lincoln, Dickens and Alliance), which were considered representative of winter wheat growing areas in the central Great Plains. At all locations, the identified optimal winter wheat cultivar under high CO₂ conditions had a larger number of tillers, larger kernel size, fewer days to flower, grew faster and had more kernels m⁻² than the check cultivar under normal CO₂ conditions. In addition, optimal sowing dates were later and optimal plant densities were smaller than under normal conditions. We concluded that RSM used in conjunction with crop and climate simulation models was useful in understanding the complex relationship between wheat genotypes, climate and management practices.