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.     

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.     

HydroLOGIC: An irrigation management system for Australian cotton

The worldwide need to improve water use efficiency within irrigated agriculture has been recognised in response to environmental concerns and conflicts in resource use. Within the Australian cotton industry, the imperative to reduce water use and optimise irrigation management through the understanding of risk, using information generated by computerised decision aids was identified and subsequently developed into the HydroLOGIC irrigation management software. This paper summarises the attributes of the HydroLOGIC irrigation management software, with particular emphasis on functionality and its application to irrigation decisions within the Australian cotton industry. The software development process is documented to provide direction for future software application initiatives, with particular emphasis on a process of user feedback, evaluation and support requirements providing direction to software development. On-farm experiments throughout the development period allowed the validation of internal software logic, irrigator decision processes, and the OZCOT cotton growth model. The software demonstrated the ability to improve yield and water use efficiency by optimising strategic and tactical irrigation decisions in the Australian furrow irrigation cotton production system. In 7 of the 11 on-farm experiments conducted, the use of HydroLOGIC helped improve overall field water use efficiency by optimising the timing of irrigation events or by indicating further irrigations would not provide yield or maturity benefits. The paper also presents useful insights into the development of software targeted for irrigation utilising in-field measurements of soil water, crop growth and a crop growth simulation model.     

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.     

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.     

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。     

作物病虫害遥感监测研究进展与展望

病虫害是农业生产过程中影响粮食产量和质量的重要生物灾害。目前,我国的作物病虫害监测方式以点状的地面调查为主,无法大面积、快速获取作物病虫害发生状况和空间分布信息,难以满足作物病虫害的大尺度科学监测和防控的需求。近年来,随着国内外卫星光谱、时间和空间分辨率的不断提升,利用遥感手段开展高效、无损的病虫害监测成为有效提升我国病虫害测报水平的重要手段。与此同时,多平台、多种方式的作物病虫害遥感监测也为病虫害的有效防治和管理提供了重要科技支撑。本文从作物病虫害光谱特征、遥感监测方法和遥感监测系统等方面阐述了作物病虫害遥感监测研究的进展,分析了当前面临的挑战,并对未来发展趋势进行了展望。     

An ecology of a DSS: reflections on managing wheat crops in the northeastern Australian grains region with WHEATMAN

An ecological framework is used to study the reinforcing and limiting processes on a computerised decision support system (DSS) designed for winter cropping decisions in the northeastern Australian Grains-belt (WHEATMAN). We found that WHEATMAN has had a significant impact on how many advisers structure their thinking and much of their advice on winter cropping in the region, but the number of routine users of WHEATMAN remains relatively low. Computer hardware was the most obvious limiting factor to widespread use during the early stages of the 15 year history of the project. However, despite a dramatic increase in the availability of computers on grain farms (from 5 to 75%), a maximum of 250 out of an estimated 4500 grain farmers in the region with computers directly use WHEATMAN. Another common limiting factor for adoption of DSS is a failure to engage with end users; yet from early days the WHEATMAN project had a high degree of extension agronomist and farmer input. We suggest that just as the debate on the adoption of DSS was dominated by discussions of computerisation in the late 1980s, notions of user involvement have dominated current debate. Experiences with WHEATMAN suggest that well designed software and a focused development team approach, good access to hardware and representative end user involvement are necessary requirements to help explain the comparative longevity of the project. On their own these are not sufficient requirements for widespread adoption or impact. We argue that the perception of farmers of the nature of dryland farm management in general, and the specific decisions addressed by WHEATMAN are the primary limitations to the routine use of a computerised DSS for tactical decision making.     

Management planning for winter wheat with multiple objectives (1): The BETHA system

Today, farmers have to fulfil the economic, technological and environmental requirements laid down by agro-industries or consumers, or government regulations. This makes designing crop management plans more complex. A tool specific for a wheat crop has therefore been devised: the BETHA system. First, BETHA generates feasible crop management plans from an agronomic model. This agronomic model includes simple relationships that link qualitative and quantitative production to the crop, the crop techniques and climate and soil characteristics. As BETHA is a knowledge-based system, the user can easily modify the agronomic model. BETHA then performs a multiple criteria analysis to classify the crop management plans according to their capacity to follow the competing requirements defined by the user. A non-totally compensatory method based on agreement and discordance principles has been used because these criteria may be very different and cannot be directly aggregated.     

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.     

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.     

农艺与物联网下的智慧农业

农业物联网在大棚控制系统中的运用实现了农作物增产、改善品质、调节生长周期和提高经济效益的目的。从物联网出发,结合农艺技术和物联网技术,提出了智慧农业系统结构总体框架,为用户实时监控农田并进行信息决策提供了技术支持,真正实现了农业管理的智能化,符合现代农业的发展。      

Using crop and pest models for management applications

Models that simulate crop production systems are useful tools to aid in crop management. The manager can evaluate numerous management options quickly and select the options most suitable for his situation. The Florida Soybean growth model (SOYGRO) has been implemented with several user interfaces to meet specific management goals. These implementations include a gaming model (SOYGAME), a pest decision model (PESTDEC), an irrigation decision model (IRRDEC) and a strategy evaluation model (SICM). The crop model integrates the effects of weather conditions, crop management inputs and soil conditions on crop yield and the yield changes are reflected in the corresponding profit. Also described in the paper is an automated weather collection and reporting system for the acquisition and use of weather data.     

基于数据库的温室作物生长管理智能决策支持系统的研制

以Delphi7．0为软件开发环境，构建了温室作物生长管理智能决策支持系统。提出了温室作物生长管理智能决策支持系统的研究思路及系统实现的技术路线，采用Database Desktop来设计系统中的知识库、模型库和数据库。以数据库为基础的系统，有助于系统对知识、模型和相关数据的管理。对系统决策的过程采用基于数据库的方法进行编写，解决了温室作物生长智能决策过程中推理困难的问题，同时提高了决策的准确程度。经实际运用达到了预期的效果。     

A rule-based method for the development of crop management systems applied to grain sorghum in south-western France

A generic approach is proposed for the development and testing of crop management systems in contrasting situations of water availability. Ecophysiological knowledge, expertise, regional references and simulation models are combined to devise management strategies adapted to production targets and constraints. The next stage consists of converting these crop management strategies into logical and consistent sets of decision rules. Each rule describes the reasoning which is used to apply a technical decision by taking account of observed or simulated environmental conditions or predicted agronomic risks.

This approach was applied to design crop management systems for grain sorghum (Sorghum bicolor L. Moench.) in south-western France. For spring-sown crops, management (sowing date, plant density, varietal choice, N fertilizer rate and timing) was based on water availability, both for economic and environmental reasons. Specific sets of decision rules were written for irrigated and rainfed conditions. The establishment of rules was based on agronomic principles (e.g. for plant density) or on the application of a simulation model (e.g. for sowing date, variety). N fertilization and irrigation were applied using combined N and water dynamic models.

A novel methodology combining crop diagnosis, analytical trials and crop simulation was developed to evaluate the management systems. An irrigated and a rainfed rule-based management system were compared near Toulouse (S.W. France) from 1995 to 2002. The profitability of rainfed low-input management was confirmed for sorghum in spite of high yields under irrigation (up to 10 t ha⁻¹). The adaptation of sorghum management in rainfed conditions was mainly achieved through early maturing cultivars and by reducing N applications by 65%.     

BP神经网络与GA-BP农作物需水量预测模型对比

农作物需水量预测是制定合理灌溉制度的重要依据.针对BP神经网络的不足,利用遗传算法(GA)具有全局搜索能力强的特点,建立基于GA-BP神经网络的农作物需水量预测模型.以广州辣木农庄试验田农作物作为研究对象,结果表明:基于BP神经网络农作物需水量预测模型测试集均方误差和确定性系数分别为0.037和0.648;GA-BP神经网络农作物需水量预测模型测试集均方误差和确定性系数分别为0.013和0.882,GA-BP农作物需水量预测模型收敛速度、确定性系数和性能均优于BP农作物需水量预测模型.     

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.     

A systematic representation of crop rotations

Crop rotations are allocations by growers of crop types to specific fields through time. This paper aims at presenting (i) a systematic and rigorous mathematical representation of crops rotations; and (ii) a concise mathematical framework to model crop rotations, which is useable on landscape scale modelling of agronomical practices. Rotations can be defined as temporal arrangements of crops and can be classified systematically according to their internal variability and cyclical pattern. Crop sequences in a rotation can be quantified as a transition matrix, with the Markovian property that the allocation in a given year depends on the crop allocated in the previous year. Such transition matrices can represent stochastic processes and thus facilitate modelling uncertainty in rotations, and forecasting of the long-term proportions of each crop in a rotation, such as changes in climate or economics. The matrices also permit modelling transitions between rotations due to external variables. Computer software was developed that incorporates these techniques and was used to simulate landscape scale agronomic processes over decadal periods.     

Operational forecasting of South African sugarcane production: Part 1 – System description

Commercial sugarcane crops in South Africa are grown under a wide range of agronomic and socio-economic conditions. These factors, together with climatic variation have resulted in a 17% variation in sugarcane production and there is considerable scope to improve productivity through accurate and timeous forecasts. This paper reports on the development of an operational crop forecasting system based on a simulation model. The country’s entire area of sugar production was subdivided into homogeneous climate zones using a wide range of data and expert opinion. These zones serve as simulation units within the system and model input and area aggregation data were obtained for each climate zone. Irrigation is simulated according to typical, zone specific strategies taking into account water use restrictions. Simulations of crops growing in the current year are completed using 10 historic seasons to substitute the remainder of the season. The selection of these seasons is based on the climate outlook. Reports containing information for national, regional and site specific cane production are generated and distributed to industry stakeholders. To the authors’ knowledge, this is the first national scale model-based operational yield forecasting system for sugarcane. Possible future improvements to the system may include stochastic input variables, more representative irrigation simulations, quantifying forecast uncertainty and providing suitable reference crop yield. The system is evaluated in another paper.     

Empirical assessment of water management institutions in northern China

We examine the development of irrigation management in northern China using data from village and household panels. During the past decade, reform-oriented institutions, such as water user associations and contracting, have largely replaced the traditional institution of collective management in village-level irrigation systems. A feature unique to China is that water user associations and contractors are provided with monetary incentives to save water. Water user associations have not yet achieved the broad-based participation of farmers that some advocates consider as a primary goal for forming the associations. Many village leaders serve also as the leaders of water user associations, thus possibly reducing opportunities for receiving operational input and policy direction from farmers. However, we observe improved performance of irrigation systems managed by water user associations, relative to collective management, in terms of maintenance expenditures, the timeliness of water deliveries, and the rates of fee collection. Performance has improved also in systems managed by contractors, although not as substantially as in the case of water user associations.