R.L. McCown

Advances in farming systems analysis and intervention

In this paper, we recognize two key components of farming systems, namely the biophysical ‘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 researchparadigm. Bioph ysical 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 withth e Agricultural Production Systems Simulator (APSIM). Two case studies are discussed, one withbroad-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. # 2001 Published by Elsevier Science Ltd. All rights reserved.

Locating agricultural decision support systems in the troubled past and socio-technical complexity of `models for management'

Although not conspicuous in its literature, agricultural modelling and its applications have inherited much from the field of operational (operations) research. In the late 1940s, techniques for mathematically simulating processes came into agricultural science directly from industry. The decision support system (DSS) concept followed almost 40 years later. It seems that the large differences between farm production and its management and industrial production and its management account for the failure of agricultural systems scientists to be more attentive students of the experiences in this parent field. In hindsight, the penalty of this is greatest in the matter of the problematic socio-technical relationship between scientific models built to guide practice and actual practice. As a socio-technical innovation, the agricultural DSS has much more in common with DSSs in business and industry than might be expected judging by the domain knowledge content. One implication is that the crisis in the parent field concerning the ‘problem of implementation’ could have served as a cautionary tale for agriculture. Although this opportunity was missed, it is not too late to tap problemstructuring and problem-solving insights from operations research/management science to aid our thinking about our own ‘problem of implementation’. This paper attempts this in constructing a framework for thinking about subsequent papers in this Special Issue. # 2002 Elsevier Science Ltd. All rights reserved.

Re-inventing model-based decision support with Australian dryland farmers. 4. Yield Prophet® helps farmers monitor and manage crops in a variable climate.

In Australia, a land subject to high annual variation in grain yields, farmers find it challenging to adjust crop production inputs to yield prospects. Scientists have responded to this problem by developing Decision Support Systems, yet the scientists’ enthusiasm for developing these tools has not been reciprocated by farm managers or their advisers, who mostly continue to avoid their use. Preceding papers in this series described the FARMSCAPE intervention: a new paradigm for decision support that had significant effects on farmers and their advisers. These effects were achieved in large measure because of the intensive effort which scientists invested in engaging with their clients. However, such intensive effort is time consuming and economically unsustainable and there remained a need for a more cost-effective tool. In this paper, we report on the evolution, structure, and performance of Yield Prophet®: an internet service designed to move on from the FARMSCAPE model to a less intensive, yet high quality, service to reduce farmer uncertainty about yield prospects and the potential effects of alternative management practices on crop production and income. Compared with conventional Decision Support Systems, Yield Prophet offers flexibility in problem definition and allows farmers to more realistically specify the problems in their fields. Yield Prophet also uniquely provides a means for virtual monitoring of the progress of a crop throughout the season. This is particularly important for in-season decision support and for frequent reviewing, in real time, of the consequences of past decisions and past events on likely future outcomes. The Yield Prophet approach to decision support is consistent with two important, but often ignored, lessons from decision science: that managers make their decisions by satisficing rather than optimising and that managers’ fluid approach to decision making requires ongoing monitoring of the consequences of past decisions.

Re-inventing model-based decision support with Australian dryland farmers. 3. Relevance of APSIM to commercial crops

Crop simulation models relevant to real-world agriculture have been a rationale for model development over many years. However, as crop models are generally developed and tested against experimental data and with large systematic gaps often reported between experimental and farmer yields, the relevance of simulated yields to the commercial yields of field crops may be questioned. This is the third paper in a series which describes a substantial effort to deliver model-based decision support to Australian farmers. First, the performance of the cropping systems simulator, APSIM, in simulating commercial crop yields is reported across a range of field crops and agricultural regions. Second, how APSIM is used in gaining farmer credibility for their planning and decision making is described using actual case studies. Information was collated on APSIM performance in simulating the yields of over 700 commercial crops of barley, canola, chickpea, cotton, maize, mungbean, sorghum, sugarcane, and wheat monitored over the period 1992 to 2007 in all cropping regions of Australia. This evidence indicated that APSIM can predict the performance of commercial crops at a level close to that reported for its performance against experimental yields. Importantly, an essential requirement for simulating commercial yields across the Australian dryland cropping regions is to accurately describe the resources available to the crop being simulated, particularly soil water and nitrogen. Five case studies of using APSIM with farmers are described in order to demonstrate how model credibility was gained in the context of each circumstance. The proposed process for creating mutual understanding and credibility involved dealing with immediate questions of the involved farmers, contextualising the simulations to the specific situation in question, providing simulation outputs in an iterative process, and together reviewing the ensuing seasonal results against provided simulations. This paper is distinct from many other reports testing the performance and utility of cropping systems models. Here, the measured yields are from commercial crops not experimental plots and the described applications were from real-life situations identified by farmers. A key conclusion, from 17 years of effort, is the proven ability of APSIM to simulate yields from commercial crops provided soil properties are well characterised. Thus, the ambition of models being relevant to real-world agriculture is indeed attainable, at least in situations where biotic stresses are manageable.

The climatic potential for beef cattle production in tropical Australia: Part I—Simulating the annual cycle of liveweight change

Abstract This is the first of a series of papers dealing with a survey of the agricultural climate as it pertains to the beef cattle industry in northern Australia. Beef cattle production here, as in most of the tropics, is characterised by an annual periodicity of weight gain and loss in train with seasonal water supply and temperatures. Trends in a weekly growth index derived from a simulated water budget and mean daily temperatures were found to correlate with trends in liveweight changes. Criteria for estimating the start and cessation of a ‘green season’ and a ‘dry season’, corresponding to the main liveweight gain and loss periods respectively, are derived and validated using cattle liveweight data from seven locations and both native and improved pastures. Linkage between cattle liveweight change and climate was close on native grass pastures but not on legume-improved pastures.

The climatic potential for beef cattle production in tropical Australia: Part III--Variation in the commencement, cessation and duration of the green season

Abstract Part I demonstrated a method for deriving from standard meteorological data a ‘green season’ and a ‘dry season’ which correspond to the main periods of liveweight gain and loss respectively of cattle on native grass pastures. This paper describes (a) the geographic variation in the incidence and duration of the green season utilising a network of 77 stations in tropical Australia, and (b) the year-to-year variability for eight of these stations. Variability in the date of commencement of the green season (the termination of the dry season) differed among stations, with ranges as low as 11 weeks and as high as 20. Differences of up to 16 weeks in the median duration of the green season between stations occurred within the study area, while the range of variation from year to year at a station was as low as 13 weeks and as high as 34. Results are compared with those of previous descriptions. Difficulties imposed on management by climatic variability are discussed.

Role of modelling in improving nutrient efficiency in cropping systems

The applicability of models in addressing resource management issues in agriculture has been widely promoted by the research community, yet examples of real impacts of such modelling efforts on current farming practices are rare. Nevertheless, simulation models can compliment traditional field experimentation in researching alternative management options. The first objective of this paper is, therefore, to provide four case study examples of where models were used to help research issues relating to improved nutrient efficiency in low-input cropping systems. The first two cases addressed strategies of augmenting traditional farming practices with small applications of chemical fertilizer (N and P). The latter two cases explicitly addressed the question of what plant genetic traits can be beneficial in low-nutrient farming systems. In each of these case studies, the APSIM (Agricultural Production Systems Simulator) systems model was used to simulate the impacts of alternative crop management systems.The question of whether simulation models can assist the research community in contributing to purposeful change in farming practice is also addressed. Recent experiences in Australia are reported where simulation models have contributed to practice change by farmers. Finally, current initiatives aimed at testing whether models can also contribute to improving the nutrient efficiency of smallholder farmers in the SAT are discussed.

The climatic potential for beef cattle production in tropical Australia: Part II-- liveweight change in relation to agroclimatic variables

Abstract Part I of this series demonstrated a method of simulating a ‘green season’ and a ‘dry season’ which corresponded closely to the main period of liveweight gain and loss respectively of cattle on native grass pastures. This paper attempts to further characterise these seasons, using agro-climatic variables derived from a weekly pasture growth index, as to their quantitative relation to cattle liveweight changes. Neither the duration of the green season nor the variable most closely related to cumulative pasture growth (‘growth weeks’) accounted for much of the variation in amount of gain in the green season. In the dry season, however, the amount of liveweight loss was closely related to the estimated number of weeks without green feed (‘dry weeks’). Extraordinary weight loss in the dry season occurred in the years with few growth weeks in the green season, indicating pasture quantity as well as quality limitations in dry season nutrition in these years. In comparison to the green season, marginal response in liveweight to additional green weeks was over two times as great when cumulated over an entire year; this suggests that the main benefit of prolonging the green season is its effect in shortening the dry season. The problem of generalising from the very few stations with cattle data is discussed.

Improvement of pressure chamber measurements of two legumes by constriction of stems

SummaryA valid determination of the balancing pressure end-point was not possible withStylosanthes hamata (L.) Taub. cv. Verano andS. scabra Vog. using a sealing stopper of the Waring and Cleary type. An exudate, apparently from the pith, occurred at low pressures even in stressed plants. The problem did not occur in a chamber fitted with a compression gland. A modification of the Waring and Cleary chamber which caused greater stem constriction proved successful.

The climatic potential for beef cattle production in tropical Australia: Part IV— variation in seasonal and annual productivity

Abstract In Part II liveweight loss in the dry season was found in most years to be closely related to cumulative ‘dry weeks’; extraordinary weight loss occurred in the dry season in years in which there were a low number of ‘growth weeks’ in the previous green season. Annual liveweight gain was related to the total number of ‘green weeks’. In this paper the geographic variation in these three agro-climatic parameters is described using a network of 77 stations across northern Australia, and the year-to-year variability is examined for eight representative stations. Variation in dry season severity was greater than variation in green season productivity (growth weeks). Median ‘dry weeks’ in the dry season varied from 29 to nil over the area. ‘Green weeks’ in the dry season as a result of winter rain is an important phenomenon in a relatively small part of the area, but in this area year-to-year variability is extremely high. It is concluded that the objectives of the study, to extend existing agro-climatic methodology to interface with cattle production and to use this in surveying the climatic potential for this form of land use over the entire tropical region of Australia, were achieved to the extent that the existing animal production data allow.