Agent-based social simulation: a method for assessing the impact of seasonal climate forecast applications among smallholder farmers

Seasonal climate forecasts provide probabilistic information on future climate on timescales of two to three months. Where this information is not presently used it is difficult to evaluate the impact it might have. In order to justify disseminating the information to marginal groups it is important that the potential impact of the forecast is explored so that the negative and positive effects are at least partially appreciated before use of the information is widely promoted. We use an agent-based social simulation model, based on empirical evidence from field work in Lesotho, to assess the impact of using seasonal forecasts among smallholder farmers. The impact of using the forecast depends on the agents' initial household characteristics, what options they choose in responding to the forecast and the trust they place in the forecast (which in turn depends on their ability to learn and to follow their neighbours). Interaction of climate, crop productivity and social factors determines how much household-agents benefit or lose, evaluated in terms of crop yields and likelihood of exhausting food storage. Adoption of the forecast has the potential to decrease starvation among marginal household-agents but poor forecasts may do more harm than good. This work suggests that if forecasts are not correct more than 60–70% of the time, then they are unlikely to benefit poor farmers. Poor forecasts, or forecasts that fail badly, when they do fail, lead to longer adoption timescales for forecast use. Further investigation into the impact of the forecast at the village level is encouraged before dissemination is actively pursued without appreciating potential impacts.     

The value of climate information when farm programs matter

Predictability of seasonal climate variations associated with ENSO suggests a potential to reduce farm risk by tailoring agricultural management strategies to mitigate the impacts of adverse conditions or to take advantage of favorable conditions. Federal farm policies may enhance or limit the usefulness of this climate information. A representative peanut–cotton–corn non-irrigated North Florida farm was used to estimate the value of the ENSO-based climate information and examine impacts of farm programs under uncertain conditions of climate, prices, and risk aversion levels. Yields from crop model simulations and historical series of prices were used to generate stochastic distributions that were fed into a whole farm model, first, to optimize crop selection and planting dates, and then, to simulate uncertain outcomes under risk aversion, with and without the use of climate information, and with and without the inclusion of farm programs. Results suggest that seasonal climate forecasts have higher value for more risk averse farmers when La Niña or El Niño ENSO phases are forecast. Highly risk averse farmers could benefit from the forecast by taking advantage of potential favorable conditions (offensive responses). The inclusion of Commodity Loan Programs (CLP) and Crop Insurance Programs (CIP) decreased the overall value of the forecast information even to negative levels. However, more risk averse farmers could still benefit moderately from El Niño and marginally from La Niña forecasts when they participate in CLP and CIP.     

Advances in application of climate prediction in agriculture

Agricultural ecosystems and their associated business and government systems are diverse and varied. They range from farms, to input supply businesses, to marketing and government policy systems, among others. These systems are dynamic and responsive to fluctuations in climate. Skill in climate prediction offers considerable opportunities to managers via its potential to realise system improvements (i.e. increased food production and profit and/or reduced risks). Realising these opportunities, however, is not straightforward as the forecasting skill is imperfect and approaches to applying the existing skill to management issues have not been developed and tested extensively. While there has been much written about impacts of climate variability, there has been relatively little done in relation to applying knowledge of climate predictions to modify actions ahead of likely impacts. However, a considerable body of effort in various parts of the world is now being focused on this issue of applying climate predictions to improve agricultural systems.

In this paper, we outline the basis for climate prediction, with emphasis on the El Niño-Southern Oscillation phenomenon, and catalogue experiences at field, national and global scales in applying climate predictions to agriculture. These diverse experiences are synthesised to derive general lessons about approaches to applying climate prediction in agriculture. The case studies have been selected to represent a diversity of agricultural systems and scales of operation. They also represent the on-going activities of some of the key research and development groups in this field around the world. The case studies include applications at field/farm scale to dryland cropping systems in Australia, Zimbabwe, and Argentina. This spectrum covers resource-rich and resource-poor farming with motivations ranging from profit to food security. At national and global scale we consider possible applications of climate prediction in commodity forecasting (wheat in Australia) and examine implications on global wheat trade and price associated with global consequences of climate prediction.

In cataloguing these experiences we note some general lessons. Foremost is the value of an interdisciplinary systems approach in connecting disciplinary knowledge in a manner most suited to decision-makers. This approach often includes scenario analysis based on simulation with credible models as a key aspect of the learning process. Interaction among researchers, analysts and decision-makers is vital in the development of effective applications — all of the players learn. Issues associated with balance between information demand and supply as well as appreciation of awareness limitations of decision-makers, analysts, and scientists are highlighted. It is argued that understanding and communicating decision risks is one of the keys to successful applications of climate prediction.

We consider that advances of the future will be made by better connecting agricultural scientists and practitioners with the science of climate prediction. Professions involved in decision making must take a proactive role in the development of climate forecasts if the design and use of climate predictions are to reach their full potential.     

Economic factors and opportunities as determinants of water use efficiency in agriculture

A consequence of water scarcity is that it has enhanced research and development aimed at increasing water use efficiency in irrigated agriculture. Although biological, genetic, and technological achievements may induce potential high levels of water use efficiency, actual observed values may be much smaller. This paper distinguishes between potential and actual water use efficiency measures, and discusses factors and opportunities that may affect actual water use efficiency in irrigated agriculture. These include economic forces, environmental effects, and institutional arrangements, each of which may affect water use decisions at field, farm, regional, and national levels. The paper develops a scheme to generally address differences between private and social considerations that determine water use efficiency decisions. Using this scheme, several examples from California and Israel are provided to support the arguments. The paper concludes that the concept of water use efficiency is much broader than its definition: yield per water applied or transpired. Moreover, determination of water use efficiency levels in irrigated agriculture is a complex issue that requires interdisciplinary considerations.     

Limits to agricultural growth in the Sistan Closed Inland Delta,Iran

The Sistan Delta in Iran is located at the end of a closed basin with nearly 100% of the supply coming from Afghanistan. This supply is supporting irrigated agriculture in the area and is the source for the lake system around the delta. These Hamoun lakes are ecological very valuable wetlands; a number of them are registered as Ramsar sites. The Iranian government tries to improve the living conditions of the people in the area, among others by providing infrastructure for irrigated agriculture. Further development of the irrigated area will mean less water for the Hamouns with resulting lower average water coverage of the lakes. This will not only endanger the ecosystem that the Hamouns support but also the livelihoods of the people that depend on the goods and services that the lakes provide. This paper describes a study that has been carried out to support decision making on potential development schemes in the delta. The non-availability of data from Afghanistan requires the development of various tools and the use of remote sensing techniques to enable to make estimates for the river flow that Iran can expect from Afghanistan. An IWRM approach has been used for the balancing of interests involved. Some preliminary conclusions are described.     

我国农业信息化发展的思路及对策

在飞速发展的当今经济社会,信息在各领域的应用显得越来越重要.将信息引入农业领域,发挥其决策支持和网络通讯优势,不仅可以加快信息技术的推广与应用,服务农业、农村和农民,而且可以提高农业劳动生产率、资源利用率和农业经济效益.为此,具体分析了我国农业信息化的发展过程和目前存在的问题,并对我国的农业信息化建设提出了建议和措施.     

Bio-economic assessment of climate change impacts on managed grassland production

In order to analyze impacts of climate change on managed grassland systems and to project potential changes in farmers’ management practices in response to altered climatic conditions, we develop a modeling approach that integrates a process-based grassland model into an economic model. This economic model describes farmers’ decision making with respect to input use and accounts for production levels, production risks, fodder quality as determined by the grassland composition, and environmental protection. We apply the bio-economic model to an intensively managed grassland system with a geographic focus on the Swiss Plateau. Our results show an increase of future production risks in grassland production due to climate change. Projected changes in yield levels, grassland composition and optimal responses of risk-averse farmers are dependent on the assumptions concerning cross-compliance obligations, forage quality and particularly on the assumed effect of elevated CO₂ concentrations: Grasslands yields will increase under future climatic conditions only if the benefits of rising atmospheric CO₂ concentrations are taken into account. Without this potential benefit, climate change will lead to less intensive input use and lower grassland yields.     

基于Web和作物模型的棉花气象服务系统构建

为便于作物模型的推广和应用,为农业气象服务提供有力工具,以棉花功能结构模型Cotton XL为基础,构建了基于Web的棉花气象服务系统,实现了新疆棉区棉花生长发育状况的逐日动态模拟。系统采用MySQL建立了新疆棉区气象、土壤和主栽品种数据库,采用B/S结构模式,包括前端展示、数据存储和后台分析,运用Java语言实现了棉花模型Cotton XL的内部调用以及模型和数据库之间的信息传递,运用PHP语言实现了决策页面和数据库之间的信息传递。系统以气候、土壤、管理措施和品种特性为基本输入,可提供棉花生长发育动态预测、产量预报和品质预测等服务。经验证,系统能够较好地反应新疆棉区棉花生长发育和产量形成过程,系统的建立对提升气象为农服务能力具有重要的现实意义。      

设施农业高效用水信息化发展及其技术体系探讨

发展设施农业用水信息化是提高水资源利用效率与效益,实现水资源优化配置的重要途径。在分析国内外设施农业用水信息化发展现状的基础上,结合信息化发展的需求,研究了设施农业高效用水信息化的技术体系,重点分析了基础数据的监测网络及共享平台、作物需水信息评价与检测、输配水系统的构成、功能及工作过程,旨在为提高设施农业用水信息化水平提供决策依据。     

BP神经网络在农业工程中的研究与应用

该文概述了BP神经网络在农机总动力预测、农业专家系统信息决策、虫情测报、农作物水分和养分胁迫、土壤墒情、变量施肥、分类鉴别和图像处理等领域的应用情况,总结了人工神经网络模型的优点,指出其在精准农业和智能农业中的重要理论技术支撑作用。      

Evolution of drought policy and related science in Australia and South Africa

Australia and South Africa are dominated by extensive agriculture, both countries being predominantly arid and exposed to a highly variable climate. Limiting land degradation, maintaining the financial viability of farms and improving the risk-management skills of farmers are common problems which government and industry in both countries are attempting to address. Both countries are currently refining their approaches to drought management, and have been making substantial use of science in improving the monitoring and assessment of drought, and the management of the land. Unlike the situation in Australia, however, South Africa's approach is being reviewed against the backdrop of a fundamental reorientation of broader agricultural policies in the context of that country's transition to a fully fledged democracy. The changing approach to drought policy in South Africa can therefore only be fully understood in the context of this broader process of change. This paper compares the climate and agriculture of Australia and South Africa, and how their different drought policies have evolved. Observations are made on the role of scientists in providing advice to political decision makers, and ways in which the process of decision making with respect to drought may be improved.     

The FARMSCAPE approach to decision support: farmers', advisers', researchers' monitoring,simulation, communication and performance evaluation

FARMSCAPE (Farmers', Advisers', Researchers', Monitoring, Simulation, Communication And Performance Evaluation) is a program of participatory research with the farming community of northeast Australia. It initially involved research to explore whether farmers and their advisers could gain benefit from tools such as soil characterisation and sampling, climate forecasts and, in particular, simulation modelling. Its current focus is facilitating the implementation of commercial delivery systems for these same tools in order to meet industry demand for their access. This paper presents the story of what was done over the past decade, it provides performance indicators of impact, it reflects on what was learnt over this period and it outlines where this research is likely to head in the future.Over the past 10 years, the FARMSCAPE team employed a Participatory Action Research approach to explore whether farmers could value simulation as a decision support tool for managing their farming system and if so, could it be delivered cost-effectively. Through farmer group engagement, on-farm trials, soil characterisation, monitoring of crops, soils and climate, and sessions to apply the APSIM systems simulator, FARMSCAPE represented a research program on decision support intervention. Initial scepticism by farmers and commercial consultants about the value of APSIM was addressed by testing its performance both against measured data from on-farm trials and against farmers' experiences with past commercial crops. Once this credibility check was passed, simulation sessions usually evolved into participants interactively inquiring of the model the consequence of alternative management options. These ‘What if’ questions using APSIM were contextualised using local climate and soil data and the farmer's actual or proposed management rules.The active participation of farmers and their advisers, and working in the context of their own farming operations, were the key ingredients in the design, implementation and interpretation of the FARMSCAPE approach to decision support. The attraction of the APSIM systems simulator to farmers contemplating change was that it allowed them to explore their own system in a manner equivalent to learning from experience. To achieve this, APSIM had to be credible and flexible. While direct engagement of farmers initially enabled only a limited number of beneficiaries, this approach generated a commercial market for timely and high quality interactions based on soil monitoring and simulation amongst a significant sector of the farming community. Current efforts are therefore focused on the training, support and accreditation of commercial agronomists in the application of the FARMSCAPE approach and tools.The FARMSCAPE approach to decision support has come to represent an approach to guiding science-based engagement with farm decision making which is being tested nationally and internationally.     

Responding to stakeholder's demands for climate information: from research to applications in Florida

Previous research shows that Florida's climate and agricultural production are influenced by the El Niño-Southern Oscillation, suggesting that farmers and ranchers might use new methods of climate forecasting to modify management, increase profits and reduce economic risks. The purposes of this paper are to describe the framework used by a Florida Consortium (FC) of researchers to assess the potential use of climate forecasts in agricultural decision-making and to summarize what was learned in the research process. The framework includes components for generation, communication and use of climate information as well as an implementation and evaluation component. Results showed that winter months are affected most by ENSO phase (higher rainfall and lower temperatures in El Niño years and the opposite during La Niña years). Yields of most crops were significantly associated with ENSO phase as were prices of some commodities. Through various mechanisms of interacting with farmers, ranchers, and extension faculty, we learned that interest in climate forecasts varied widely from highly optimistic to skeptical, and that these clients had good ideas of how to vary management if they have good forecasts. Case studies aimed at understanding potential value and risks associated with use of climate forecasts were conducted for winter fresh market tomato, cow-calf operations, and peanut production. Analytical results, confirmed by interactions with clients, showed significant value in using climate forecasts to alter specific decisions. Risks of using climate information varied among commodities, with considerable risk found in tomato due to the strong link between production and price. Perhaps the most important lesson learned was the importance of engaging trusted advisors in research and outreach efforts. A major output of the project was the close cooperation established between the FC and the Florida Cooperative Extension Service. Prospects for sustaining a climate information program in Florida are high due to joint research and extension initiatives.     

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.     

Changing systems for supporting farmers' decisions: problems,paradigms, and prospects

All correct reasoning is a grand system of tautologies, but only God can make direct use of that fact. The rest of us must painstakingly and fallibly tease out the consequences of our assumptions. (Herbert Simon in ‘The Sciences of the Artificial’, p.15)

Decision support systems (DSS), like other information systems (IS) before them, were designed to serve functions deemed by ‘management scientists’ to be potentially useful to managers. But the unwelcome fact is that the use of agricultural DSSs by managers of farms has been low. This paper probes possible reasons for this through interpretation of agricultural DSS case histories and several strands of relevant social theory. From nine cases of DSS development effort and 14 products interpreted comparatively, a number of generalisations are made that serve as reference points in the following search for explanation in theory.First, the nature of management practice of family farms is explored and differences between the internal structure governing personal action and the scientific approach to practice are contrasted. Next, the interaction between the nature of the particular action/practice and the nature of the DSS is explored. A DSS designed to provide integrated, optimal recommendations for management typifies the DSS as a proxy for a manager's decision process. Examples of elaborate expert systems that simply were not used dramatically illustrate the resistance of family farmers to have their decision processes by-passed. On the other hand, the DSS designed to serve as a tool in a modified decision process is shown to have experienced higher use, by deriving and exploiting ‘deep,’ abstract information about the system, by introducing a powerful ‘logic,’ or a combination of both.A number of the referenced case stories demonstrate the resurgence of the decision support mode whereby the simulator is in the hands of an expert intermediary as an alternative to easy-to-use software in the hands of a farmer. This is the mode of operational research/management science, which preceded the DSS.In comparison with hierarchical organizations, available options for overcoming the persistent ‘problem of implementation’ of the DSS in family farms are inherently weak. This focuses attention on the importance of the relationship between the DSS developer and the potential user. Drawing on a classic typology of possible configurations of ‘understanding’ between the scientist and the manager, four approaches to intervention are discussed. Three entail a degree of engagement that qualifies them as ‘participative.’ But one of these constitutes a departure from the DSS and broader IS traditions that places it in another paradigm. In this ‘mutual understanding’ relationship, intervention intent shifts from educating and persuading to recognition of and respect for other ways of viewing the world. This opens up the opportunities for co-creating information systems that utilise the comparative advantages of both practical and scientific knowledge. Intervention emphasis shifts from prescribing action to facilitating learning in actions.Although the DSS has fallen far short of expectations in its influence on farm management, the experience has been instructive in multiple ways to both farmers and professionals in agriculture. In many cases, farmers learned from the DSS and could then jettison it without loss. From disappointments scientists have sometimes learned what was needed to achieve a better outcome. From collated DSS experiences, important lessons for the future can be drawn.The paper concludes by conjecturing that the future of the DSS and related ISs, while more limited than once imagined, holds promise in four directions: a ‘small’ tool for aiding farmers' tactical decisions; a versatile simulator as a consultant's tool; a versatile simulator as the core of a facilitated ‘learning laboratory,’ and a formal framework that supports regulatory objectives in constraining and documenting farming practice.     

Using hierarchical systems aggregation to model the value of information in agricultural systems: An application for climate forecast information

The process of interdisciplinary research in aggregating sub-system models to model larger systems is disucussed and applied to valuing midwestern (USA) crop climate forecasts. Elements of climate forecast information schemes and characteristics of users and their environment which give rise to or restrict climate forecast information value in corn and soybean production are identified. Methods of varying these elements are applied to determine their effect on the value of climate forecast schemes.     

作物生长模型研究现状与展望

作物生长模型由最初的作物生长发育模型发展到农业决策支持模型,在科学研究、农业管理、政策制定等方面发挥着越来越重要的作用。本文首先回顾了作物生长模型的发展过程,并按照模型主要驱动因子,将作物生长模型分为土壤因子、光合作用因子和人为因子驱动3类并分别进行了归纳阐述;然后对典型的模型分别从模型模块、时空尺度、可模拟的作物类型等方面进行列表式对比;并对作物生长模型在气候变化评估、生产管理决策支持、资源管理优化等方面的应用,以及面临的极端条件、复杂农业景观和模型复杂度等挑战进行了总结,在此基础上认为遥感数据同化和孪生农场是其发展方向。     

Using AquaCrop to derive deficit irrigation schedules

Straightforward guidelines for deficit irrigation (DI) can help in increasing crop water productivity in agriculture. To elaborate such guidelines, crop models assist in assessing the conjunctive effect of different environmental stresses on crop yield. We use the AquaCrop model to simulate crop development for long series of historical climate data. Subsequently we carry out a frequency analysis on the simulated intermediate biomass levels at the start of the critical growth stage, during which irrigation will be applied. From the start of the critical growth stage onwards, we simulate dry weather conditions and derive optimal frequencies (time interval of a fixed net application depth) of irrigation to avoid drought stress during the sensitive growth stages and to guarantee maximum water productivity. By summarizing these results in easy readable charts, they become appropriate for policy, extension and farmer level use. We illustrate the procedure to derive DI schedules with an example of quinoa in Bolivia. If applied to other crops and regions, the presented methodology can be an illustrative decision support tool for sustainable agriculture based on DI.     

Agricultural systems in transition

A characteristic of modern systems of agriculture is a propensity to disturbance and instability. In appropriate circumstances the development of irrigation may be seen as a potential farm management strategy for those seeking to offset uncertainty and risk. The transition to irrigation agriculture in parts of the Namoi and Gwydir river basins in northwestern New South Wales provides an opportunity to identify the forces at work generating change. The decision to adopt or reject irrigation is examined with reference to the physical resource base, economic considerations and the environmental and institutional setting. Ultimately, however, the rate of irrigation development reflects the human behavioural response and reaction to the opportunity presented.     

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.