Advances in farming systems analysis and intervention

In this paper, we recognize two key components of farming systems, namely the bio-physical ‘Production System’ of crops, pastures, animals, soil and climate, together with certain physical inputs and outputs, and the ‘Management System’, made up of people, values, goals, knowledge, resources, monitoring opportunities, and decision making. Utilising upon these constructs, we review six types of farming systems analysis and intervention that have evolved over the last 40 years, namely: (1) economic decision analysis based on production functions, (2) dynamic simulation of production processes, (3) economic decision analysis linked to biophysical simulation, (4) decision support systems, (5) expert systems, and (6) simulation-aided discussions about management in an action research paradigm. Biophysical simulation modelling features prominently in this list of approaches and considerable progress has been made in both the scope and predictive power of the modelling tools. We illustrate some more recent advances in increasing model comprehensiveness in simulating farm production systems via reference to our own group's work with the Agricultural Production Systems Simulator (APSIM). Two case studies are discussed, one with broad-scale commercial agriculture in north-eastern Australia and the other with resource poor smallholder farmers in Africa. We conclude by considering future directions for systems analysis efforts directed at farming systems. We see the major challenges and opportunities lying at the interface of ‘hard’, scientific approaches to the analysis of biophysical systems and ‘soft’, approaches to intervention in social management systems.     

Infusing the use of seasonal climate forecasting into crop management practice in North East Australia using discussion support software

Seasonal climate forecasting offers potential for improving management of crop production risks in the cropping systems of NE Australia. But how is this capability best connected to management practice? Over the past decade, we have pursued participative systems approaches involving simulation-aided discussion with advisers and decision-makers. This has led to the development of discussion support software as a key vehicle for facilitating infusion of forecasting capability into practice. In this paper, we set out the basis of our approach, its implementation and preliminary evaluation. We outline the development of the discussion support software Whopper Cropper, which was designed for, and in close consultation with, public and private advisers. Whopper Cropper consists of a database of simulation output and a graphical user interface to generate analyses of risks associated with crop management options. The charts produced provide conversation pieces for advisers to use with their farmer clients in relation to the significant decisions they face. An example application, detail of the software development process and an initial survey of user needs are presented. We suggest that discussion support software is about moving beyond traditional notions of supply-driven decision support systems. Discussion support software is largely demand-driven and can compliment participatory action research programs by providing cost-effective general delivery of simulation-aided discussions about relevant management actions. The critical role of farm management advisers and dialogue among key players is highlighted. We argue that the discussion support concept, as exemplified by the software tool Whopper Cropper and the group processes surrounding it, provides an effective means to infuse innovations, like seasonal climate forecasting, into farming practice.     

Decision-making processes for crop management on African farms. Modelling from a case study of cotton crops in northern Cameroon

Recent work on decision processes on French farms and irrigated systems in Africa has shown that farmers plan their cyclical (recurrent) technical operations, and that one can model this planning process. Taking the case of cotton crop management in North Cameroon, we show that with certain adjustments, modelling of this kind can also be done for rainfed crop farming in Africa. The adjustments are needed to take account of the differences in social status between different fields on one farm and the implications of the fact that farm work is primarily manual. This produces decision models with a similar structure to that described for technical management of an annual crop break in a temperate climate using mechanised implements. Not only do these models give us a detailed understanding of the variability of farming practices, we can also classify them into categories according to weather scenarios yield level as a function of weather scenario. We show that one can attribute farms to these types of model using simple indicators concerning work organisation. By analysing North Cameroon farmers' decision processes for managing cotton crops we can thus produce an effective tool for organising technical supervision of farmers at the regional level: advisers can work with these decision model types by measuring some simple indicators at farm level to predict which types of model are applicable, without the onerous work of constructing individual decision models.     

On farm testing of integrated nutrient management strategies in eastern Uganda

This paper reports on a Participatory Learning and Action Research (PLAR) process that was initiated in three villages in eastern Uganda in September 1999 to enable small-scale farmers to reverse nutrient depletion of their soils profitably by increasing their capacity to develop, adapt and use integrated natural resource management strategies. The PLAR process was also used to improve the participatory skills and tools of research and extension personnel to support this process. The farming systems of the area were characterised for socio-economic and biophysical conditions that included social organisations, wealth categories, gender, crop, soil, agro forestry and livestock production. Farmers identified soil fertility constraints, their indicators, and causes of soil fertility decline, and suggested strategies to address the problem of soil fertility decline. Soil fertility management diversity among households indicated that most farmers were not carrying out any improved soil fertility management practices, despite previous research and dissemination in the area. Following the diagnosis stage and exposure visits to other farmer groups working on integrated soil fertility projects, the farmers designed 11 experiments for on-farm testing. One hundred and twenty farmers then chose, for participatory technology development, sub-sets of these 11 experiments, based on the major agricultural constraints and the potential solutions identified and prioritised by the farmers. Quantitative and qualitative results from the testing, farmer evaluation and adaptation, training, dissemination strategies and socio-economic implications of these technologies are discussed.     

GAMEDE: A global activity model for evaluating the sustainability of dairy enterprises. Part II – Interactive simulation of various management strategies with diverse stakeholders

GAMEDE is a stock-flow dynamic simulation model designed with farmers to represent dairy farm functioning and the consequences of the farmer’s daily management decisions for whole-farm sustainability. Sustainability is evaluated according to its three pillars: technico-economic viability, respect for environment, and social liveability. The model provides original information for a better understanding of the processes regulating nitrogen dynamics within the farm, and the factors determining farmers’ decisions and practices. Model implementation experiments have revealed that GAMEDE is also a useful tool to support discussions and to generate knowledge exchange among various stakeholders who play an important role in the development of farm sustainability: farmers, extension agents and researchers.While a majority of researchers and advisers are specialised and a majority of farmers fix their attention on specific and narrow themes of farm management, such a comprehensive model can help stakeholders complement their knowledge to gain a holistic view of the farming system. This holistic and integrated view is crucial: (i) for researchers who wish to explain diversity in farming systems and understand decisional and biophysical processes and their interrelated effects operating in such complex agro-ecosystems, (ii) for advisers whose aim is to define alternative management strategies applicable in practice, i.e. taking into account farm specificities, and (iii) for farmers who must choose practices compatible with their resources, assets, constraints and objectives.Holism can also improve versatility and thus the generic character of models. Issues are narrowly specified and greatly vary both among categories of stakeholders (e.g. scientists versus farmers) and within each category (e.g. among farmers). A comprehensive model that: (i) details all farm management operations, and (ii) represents their effects on different spatio-temporal levels and on the three sustainability dimensions, is more likely to respond to the various issues facing different stakeholders. We argue that capacity of models to respond to stakeholders’ questions has to be considered in future evaluations of decision support systems.     

Adaptation to Agenda 2000 (CAP reform) and optimisation of the farming system of French suckler cattle farms in the Charolais area: a model-based study

French suckler farmers need advice on the implications of the Agenda 2000 CAP reform for their farms and, in particular, on the incentives it offers for a more extensive mode of production. To support the dialogue between advisers and farmers, and thus help farmers with their decision-making, we constructed a linear programming (LP) model that optimises the farming system of the northern Massif Central Charolais suckler cattle farms, which may be either mixed (crop-livestock) or specialised (livestock). This model, called Opt'INRA, incorporated all of the production activities presently encountered in this zone, together with the constraints of the CAP premium attributions. We used it to study how, on the basis of their 1999 data, two farms, representing two situations frequently encountered in the Charolais area (a mixed crop-livestock farm and a specialised livestock farm), could best adapt to Agenda 2000.According to the model, for both of the farms studied, the economic impact of Agenda 2000 is relatively low, albeit negative. The adaptation of the system when possible does not lead to a significant increase in the gross margin of this farms. Agenda 2000 did not encourage farmers to extensify their farming system. On the other hand, this CAP reform discourages them from intensifying.     

Increasing profits and reducing risks in crop production using participatory systems simulation approaches

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 socio-economic variability and change.     

Optimal on-farm irrigation scheduling with a seasonal water limit using simulated annealing

As water resources are limited and the demand for agricultural products increases, it becomes increasingly important to use irrigation water optimally. At a farm scale, farmer's have a particularly strong incentive to optimize their irrigation water use when the volume of water available over a season is production limiting. In this situation, a farmer's goal is to maximize farm profit, by adjusting when and where irrigation water is used. However, making the very best decisions about when and where to irrigate is not easy, since these daily decisions require consideration of the entire remaining irrigation season. Future rainfall uncertainty further complicates decisions on when and which crops should be subjected to water stress. This paper presents an innovative on-farm irrigation scheduling decision support method called the Canterbury irrigation scheduler (CIS) that is suitable when seasonal water availability is limited. Previous optimal scheduling methods generally use stochastic dynamic programming, which requires over-simplistic plant models, limiting their practical usefulness. The CIS method improves on previous methods because it accommodates realistic plant models. Future farm profit (the objective function) is calculated using a time-series simulation model of the farm. Different irrigation management strategies are tested using the farm simulation model. The irrigation strategies are defined by a set of decision variables, and the decision variables are optimized using simulated annealing. The result of this optimization is an irrigation strategy that maximizes the expected future farm profit. This process is repeated several times during the irrigation season using the CIS method, and the optimal irrigation strategy is modified and improved using updated climate and soil moisture information. The ability of the CIS method to produce near optimal decisions was demonstrated by a comparison to previous stochastic dynamic programming schedulers. A second case study shows the CIS method can incorporate more realistic farm models than is possible when using stochastic dynamic programming. This case study used the FarmWi$e/APSIM model developed by CSIRO, Australia. Results show that when seasonal water limit is the primary constraint on water availability, the CIS could increase pasture yield revenue in Canterbury (New Zealand) in the order of 10%, compared with scheduling irrigation using current state of the art scheduling practice.     

The role of on-farm trails in the evaluation of composite technologies: The case of alley farming in Southern Nigeria

This paper, which draws on the experience of ILCA's Humid Zone Programme, discusses the role of on-farm research in the evaluation of alley farming. It argues that, because of the composite nature of alley farming as a technology, two distinct types of trials are required for its on-farm development. The first is concerned with the refinement of the system and the assessment of its relevance and acceptability to farmers while the second is for the collection of technical and productivity data under farm conditions. Further, because the former type of trial is concerned with the evolution and definition of the system, it must be implemented at an early stage, as part of the development of the technology, and must precede the collection of technical data. Feedback from both types of trial is essential to ensure the relevance of on-station research. The close involvement of extension is required for the successful implementation of this approach.     

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.     

Development and evaluation of an integrated simulation model for assessing smallholder crop-livestock production in Yucatán, Mexico

Mixed farming systems constitute a large proportion of agricultural production in the tropics, and provide multiple benefits for the world’s poor. However, our understanding of the functioning of these systems is limited. Modeling offers the best approach to quantify outcomes from many interacting causal variables in these systems. The objective of this study was to develop an integrated crop-livestock model to assess biophysical and economic consequences of farming practices exhibited in sheep systems of Yucatán state, Mexico. A Vensim™ dynamic stock-flow feedback model was developed to integrate scientific and practical knowledge of management, flock dynamics, sheep production, partitioning of nutrients, labor, and economic components. The model accesses sheep production and manure quantity and quality data generated using the Small Ruminant Nutrition System (SRNS), and interfaces on a daily basis with an Agricultural Production Systems Simulator (APSIM) model that simulates weather, crop, and soil dynamics. Model evaluation indicated that the integrated model adequately represents the complex interactions that occur between farmers, crops, and livestock.     

Emerging consensus on desirable characteristics of tools to support farmers’ management of climate risk in Australia

The prospect that decision support systems (DSS) can help farmers adjust their management to suit seasonal conditions by putting scientific knowledge and rational risk management algorithms at farmers’ fingertips continues to challenge the science and extension community. A number of reviews of agricultural DSS have called for a re-appraisal of the field and for the need to reflect on past mistakes and to learn from social and management theory. The objective of this paper was to investigate whether there is an emerging consensus, among stakeholders in DSS for Australian agriculture, about the lessons learned from past experience with DSS tools. This investigation was conducted in three parts. The first part was a distillation of suggestions for best practice from the relevant literature. The second part was a reflection on what the champions of five current DSS development and delivery efforts in Australia learned from their recent efforts. The third part tested the level of support for the combined findings from the first and second approaches by surveying 23 stakeholders in the research, development, delivery and funding of DSS.The key propositions relating to best practice that were supported by the survey, listed according to the strength of support, were: 1. It is essential to have a plan for delivery of the DSS beyond the initial funding period. 2. DSS need to be embedded in a support network consisting of farmers, consultants and researchers. 3. DSS development requires the commitment of a critical mass of appropriately skilled people. 4. A DSS should aim to educate farmers’ intuition rather than replace it with optimised recommendations. 5. A DSS should enable users to experiment with options that satisfy their needs rather than attempt to present ‘optimised’ solutions. 6. DSS tools stand on the quality and authority of their underlying science and require ongoing improvement, testing and validation. 7. DSS development should not commence unless it is backed by marketing information and a plan for delivery of the DSS beyond the initial funding period.While the DSS stakeholders supported the proposition that it is essential to have a plan for delivery of a DSS beyond the funding period, the majority resisted the notion of DSS development being market-driven and especially commercial delivery of DSS. We argue that since public funding of the delivery of DSS for farmers’ management of climate risk is highly unlikely, reaping the benefits of lessons learned from past efforts will require that DSS stakeholders change their perception of the commercial delivery model or find an alternative way to fund the delivery of DSS beyond the R&D phase.     

An integrated sustainability assessment of mediterranean sheep farms with different degrees of intensification

In the European Mediterranean basin, pasture-based sheep farming systems are mostly located in marginal/High Nature Value areas. These production systems are multifunctional, and their economic, environmental and social roles are equally important and recognised by policy makers and by society. However, the number of animals and holdings is decreasing, and there is great uncertainty regarding the reproducibility of these farming systems, which depends on many internal and external farm factors and their interactions. The aim of this paper was to perform a comprehensive assessment of sustainability in different sheep farming systems in north eastern Spain using the MESMIS framework. We followed a case-study approach to perform an in-depth investigation of 4 sheep meat and dairy farms with different intensities of reproduction management. Critical points of sustainability, including weaknesses and opportunities, were obtained using a participatory process with stakeholders (farmers and technical advisers) that resulted in the selection of 37 sustainability indicators that were classified according to the systemic attributes defined by MESMIS (productivity, stability, self-reliance, adaptability, equity) and according to the classical sustainability pillars (social, economic and environmental). Some underlying patterns could be observed when analysing sustainability pillars, attributes and indicators. A positive relationship between productivity and intensification level in meat farms was observed; however, economic sustainability was determined not only by on-farm but also by off-farm activities. The economic efficiency of farming (without considering subsidies) was mainly explained by the capture of added value in the dairy systems and the combination of high animal productivity as well as high forage and feed self-sufficiency in the meat systems. Social issues were also central to explaining sustainability at the farm level, including the prospects of generational turnover and the manner in which farmers perceive and rate their activity. A clear trade-off between economic and environmental indicators was observed, i.e., the higher the economic sustainability, the lower the environmental sustainability. Each farm scored differently for diverse attributes, pillars and individual indicators. The scores differed according to size, structure, resource availability and managerial skills, which implies that it would be difficult to apply a holistic sustainability analysis to farming systems instead of individual farms. A number of methodological questions arose during the evaluation process relative to the stakeholders perception of these indicators, their relevance and meaning, the reference values for comparison, or their validity to assess sustainability across spatial and temporal scales. These questions are discussed in the paper.     

Erosion/productivity modelling of maize farming in the Philippine uplands: Part I: parameterising the Agricultural Production Systems Simulator

This paper describes the parameterisation of the Agricultural Production Systems Simulator (APSIM) model to simulate open-field farming and intercropping of maize with leguminous shrub hedgerows. Whenever possible, parameters for the model were determined from measured or standard values for the environment of the field trials, while other parameters were derived from previous modelling experience in tropical environments. The remaining parameters were derived using step-wise calibration, where one or two parameters were calibrated against closely related measured data. Once parameterised, APSIM gave acceptable predictions of maize yields and soil loss from open-field farming and hedgerow intercropping. The version of APSIM described in this paper is used to simulate maize yields and soil erosion from open-field farming and hedgerow intercropping in the second paper in this series (Nelson et al., this issue). In the third paper, Nelson et al. (this issue) use cost–benefit analysis to compare the economic viability of hedgerow intercropping relative to traditional open-field farming of maize in relatively inaccessible upland areas.     

A methodology for up-scaling irrigation losses

This paper presents a methodology for up-scaling field irrigation losses and quantifying relative losses at the irrigation area level for potential water savings. Two levels of analysis were considered: First, the field level where irrigation is applied. Second, the irrigation area level, where the field level losses are aggregated, or up-scaled, using average loss functions. In this up-scaling approach, detailed crop-soil-water modelling can capture the variability of physical parameters (such as soils, crops, water table depth, and management practices) at the field level which are then used to derive loss functions for aggregating losses at higher scales (irrigation area level). This allows potential field-level adaptations and water management changes made by individual farmers to be assessed for impact at the larger irrigation area level. The APSIM farming systems model was used for simulation of crops (wheat, rice, and soybean) and their interaction with the wider system processes at the field level. Given the climate, soil, and management information (sowing, fertilisation, irrigation, and residue management), the model simulates infiltration, the soil moisture profile, plant water uptake, soil evaporation, and deep drainage on a daily basis. Then, by placing the field level analysis in the context of the wider irrigation system or catchment, it is possible to correlate field level interventions (e.g. water savings measures) with water requirements at these higher levels. Application of this method in the Coleambally Irrigation Area in NSW, Australia, demonstrated that an exponential function can describe the relationship between deep drainage losses and the water table depth for different soil, crop, and water table depth combinations. The rate of loss increase (slope of the curve) with the water table depth is higher on lighter (higher intake rates) soils than on heavy soils and is more pronounced in areas under rice cultivation. We also demonstrate that this analysis technique can assist in identifying spatial distribution of losses in irrigation areas, considering water table depth as an additional factor, leading to targeted areas for water-saving measures.     

APSIM's water and nitrogen modules and simulation of the dynamics of water and nitrogen in fallow systems

APSIM (Agricultural Production Systems Simulator) is a software system which provides a flexible structure for the simulation of climatic and soil management effects on growth of crops in farming systems and changes in the soil resource. The focus of this paper is the predictive performance of APSIM for simulation of soil water and nitrate nitrogen in contrasting soils (vertisols and alfisols) and environments. The three APSIM modules that determine the dynamics of water, carbon, and nitrogen in the soil system (viz. SOILWAT, SOILN and RESIDUE v.1) are described in terms of the processes represented, with particular emphasis on aspects of their coding that differ from their precursors in CERES and PERFECT. The most fundamental change is in SOILN, which now provides a formal balance of both carbon and nitrogen in the soil and includes a labile soil organic matter pool that decomposes more rapidly than the bulk of the soil organic matter. Model performance, in terms of prediction of soil water and nitrate, is evaluated during fallows, thereby avoiding complications arising from water use and nitrogen uptake by a crop. One data set is from a long-term experiment on a vertisol in southeast Queensland which studied two tillage treatments (conventional and zero tillage) in combination with fertiliser nitrogen inputs for the growth of wheat; soil water and nitrate were measured twice each year (pre-planting and post-harvest). The second comes from experiments at Katherine, Northern Territory, where legume leys growing on alfisols were chemically killed and ensuing changes in soil water and nitrate were measured during a single season. For both datasets, the predictive ability of the model was satisfactory for water and nitrate, in terms of both the total amounts in the whole profile and their distribution with depth. Since neither of these datasets included measurements of the runoff component of the water balance, this aspect of model performance was evaluated, and shown to be generally good, using data from a third source where runoff had been measured from contour bay catchments.     

DAIRYPRO—a knowledge-based decision support system for strategic planning on sub-tropical dairy farms. I. System description

DAIRYPRO is a combination decision support and expert system consisting of two modules. The system is designed to help dairy farmers in northern Australia make strategic decisions about their farm. It can be run by dairy extension officers as a consultation package for farmers. The system is based on a combination of statistical models developed from real farm survey data and opinions from experts in the field of dairy farming. The first module gathers together the data needed to run predictive models and the system of rules that enable the program to make estimates of regional average production (using predictive statistical models) and achievable production (using heuristics). These predictions can be compared to the farmer's actual production. Farmers are then encouraged to make hypothetical changes to the inputs on their farm, and `what-if' scenarios of increased or decreased milk production are displayed. The profit or loss associated with these changes is determined. The second module of DAIRYPRO uses the `rules of thumb' of an expert to determine how four pre-defined components of the dairy farm compare to optimum performance. These components are: the winter feeding program, summer feeding program, concentrate feeding program and capital and labour inputs. DAIRYPRO is a useful decision support package for dairy farmers, bank managers, loans officers and farm consultants. ©     

Co-evolutionary scenarios of intensification and privatization of resource use in rural communities of south-western Niger

Agricultural production in the semi-arid agro-ecosystems of the Sahel centres on cereal staple crops and pastoralism with increasing crop–livestock integration. Animals mobilize soil fertility through manure production, graze crop by-products, and transfer nutrients from distant pastures to cropped areas. Yet in these systems various interacting factors, i.e. climate variability, poor soil fertility, poverty, and institutional constraints limit the capacity of agriculture to keep pace with the growing needs of an increasing human population.The major trends associated with population growth are (1) increasing area cropped at the expense of rangelands; (2) reduced availability of and access to good quality grazing resources, and (3) seasonal migration of labourers and transhumance of herds. These trends lead to co-evolution of farming systems towards increased privatisation of resource use.This study examines the implications of the development processes where farming systems co-evolve with their surroundings. It explores the impact of integrated management of livestock and crops in rural communities on both the livelihoods of differently endowed farms, and on the agro-ecosystem. Different scenarios explored the co-evolution of three sites situated in Western Niger with their environment. The sites differ in relative area cropped. The scenarios simulate the different future outcomes for varying socio-economic and biophysical criteria with either current or more intensive management.Explorative bio-economic models are used to compare a range of farm, livelihood and ecological indicators, and to reveal social and ecological trade-offs.If current agro-ecosystems and their environments co-evolve towards increased privatisation of grazing resources, then soil fertility is likely to deteriorate on the lands managed by the agro-pastoral groups. Soil fertility may improve on lands managed by the livestock-scarce farmers settled in villages, at the cost of declining farm incomes. The agro-pastoral groups are likely to resort to more distant pastures for feed. The village-based, livestock-endowed farms will resort to feeding on on-farm crop residues. Intensification, though associated with relative decreases in real incomes, will enhance food security in these new systems, except for the poorer settled farmers.     

Evaluation of the water use efficiency of alternative farm practices at a range of spatial and temporal scales: A conceptual framework and a modelling approach

Water is the principal limiting resource in Australian broadacre farming, and the efficiency with which farmers use water to produce various products is a major determinant both of farm profit and of a range of natural resource management (NRM) outcomes. We propose a conceptual framework based on multiple water use efficiencies (WUEs) that can be used to gain insight into high-level comparisons of the productivity and sustainability of alternative farming practices across temporal and spatial scales. The framework is intended as a data aggregation and presentation device. It treats flows of water, biomass and money in a mixed farming system; economic inefficiencies in these flows are tracked as they are associated with a range of NRM indicators.We illustrate the use of the framework, and its place in a larger research programme, by employing it to synthesise the results from a set of modelling analyses of the effect of land use choices on long-term productivity and a range of NRM indicators (frequency of low ground cover, deep drainage, N leaching rates and rate of change in surface soil organic carbon). The analyses span scales from single paddocks and years to whole farms and have been carried out with the APSIM and GRAZPLAN biophysical simulation models and the MIDAS whole-farm economic model.In single wheat crops in one study, different land uses in preceding years affect grain yield primarily by affecting the harvest index. When the scale changes to cropping rotations, the critical factor affecting overall water use efficiency is found to be the proportion of stored soil water that is transpired by crops. When ordinated in terms of their water use efficiencies, a set of 45 modelled rotation sequences at another location are differentiated mainly by the proportion of pasture in the rotation; when rotations are ordinated using key NRM indicators, the proportion of lucerne pasture is the main distinguishing factor. Finally, we show that for whole crop-livestock farms at three different locations across southern Australia, the pattern of water use efficiencies in the most profitable farming systems changes in similar ways as cropping proportion is altered. At this scale, land use choices affect multiple water use efficiency indices simultaneously and commodity prices determine the balance of the resulting economic tradeoffs.Limitations to the use of the WUE framework arising from its relative simplicity are discussed, as are other areas of farming systems research and development to which it can be applied.