The increasingly chaotic nature of rainfall in semi-arid climates challenges crop growers to balance nitrogen fertiliser inputs for both food security and environmental imperatives. Too little ...nitrogen restricts yields and runs down soil organic carbon, while too much nitrogen is economically wasteful and environmentally harmful. The degree to which crop-water and crop-nitrogen processes combine to drive yields of rainfed wheat crops is not well understood or quantified. Here we investigate two comprehensive Australia-wide data sets, one from commercial wheat growers' fields and the other from systematic simulation of 50 sites by 15 years using a comprehensive mechanistic cropping system model. From these data, we derived a simple model combining water use with available nitrogen and their interaction. The model accounted for 73% of the variation in the simulated yield data and 46% of the variation in the growers' yield data. We demonstrate how the simple model developed here can be deployed as a tool to aid growers' in-crop nitrogen application decisions.
The Food and Agriculture Organization estimates that more than 800 million people engage in urban agriculture producing more than 15% of the world's food. Recently, there has been a resurgence of ...interest in urban agriculture in many wealthy, developed cities, with new technology and agro-architecture being employed to grow food in cities at commercial scale. This has been accompanied by an increase in media coverage. Big claims are being made, including that urban agriculture can decrease greenhouse emissions, ‘climate proof’ farms, help solve food security for growing urban populations and provide chemical free food with no risk of pests and diseases. Many of these claims need to be rigorously tested to ensure that sound investments can be made in enterprises that are financially viable and capable of delivering on claims of social and environmental benefits. Around the world, traditional broadacre and horticulture farming have been underpinned by years of biological, chemical, physical, economic and social research. Urban agriculture needs similar support as the industry grows and develops around the world. There are opportunities to improve crop yields and quality by pairing advancements in environmental controls, phenomics and automation with breeding efforts to adapt traits for architecture, development and quality (taste and nutrition) allowing a more diverse set of crops to be grown in controlled-environment farms. Urban farms are uniquely placed to take advantage of urban waste energy, water and nutrients but innovations are needed to use these resources safely and economically. This review discusses the technological research and innovations necessary for urban agriculture to meet the nutritional requirements of growing urban populations.
•Urban farming is experience a resurgence in interest and investment•Urban agriculture needs research support as the industry grows and develops•There are opportunities to improve yields and quality by pairing advancements in environmental controls with plant breeding•Adapting crop architecture, development and quality will allow a more diverse set of crops to be grown in urban farms•Innovations are needed to allow UA can take advantage of waste energy, water and nutrient resources safely and economically
► Australian stakeholders surveyed on the implementation problem in agricultural DSS. ► Literature-based best practice propositions on DSS development were supported. ► Most but not all DSS delivery ...propositions were supported by stakeholders. ► There is a lack of consensus about commercial versus publicly funded DSS delivery. ► A sustainable means of embedding DSS into farmers’ knowledge networks still needed.
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.
► Producing more food per unit resource use, while minimising the impact of food production on the environment, will require increased precision in the use of inputs and reduction in inefficiencies ...and losses in farming systems. ► We focus on four technologies and production systems emerging in Australian agriculture: climate risk management; precision agriculture; crop–livestock integration and deficit irrigation. ► For each of these systems we identify how well they are likely to match desirable attributes of an ecological intensification of agriculture system. ► There is scope for emerging and future technologies to progress Australian agriculture towards greater productivity and ecological efficiency.
World population growth, changing diets and limited opportunities to expand agricultural lands will drive agricultural intensification in the decades ahead. Concerns about the reliance of past agricultural intensification on non-renewable resources, about its negative impacts on natural resources both on and off farm and on greenhouse gas emissions, provide an imperative for future agricultural intensification to become ecologically efficient. We define ecological intensification of agriculture (EIA) as: producing more food per unit resource use while minimising the impact of food production on the environment. Achieving it will require increased precision in the use of inputs and reduction in inefficiencies and losses. It will also require a more holistic view of farming, going beyond efficiencies of single inputs into a single field in a single season to consideration of efficiencies of whole systems over decades. This paper explores the ecological intensification issues facing agricultural production in Australia where opportunities for agricultural intensification are centred on more efficient use of limited and unreliable water resources in both dryland and irrigated agriculture. Ecological efficiencies can be achieved by better matching the supply of nutrients to crops’ requirements both temporally and spatially. This has the added benefit of minimising the opportunities for excessive nutrients to impact on soil health (acidity and dryland salinity) and water quality (pollution of groundwater and eutrophication of lakes and rivers). Opportunities for ecologically efficient intensification are also identified through better integration of crop and livestock enterprises on mixed crop–livestock farms. We define nine desirable attributes of an EIA system: (1) increased agricultural production; (2) efficient use of limited resources; (3) minimal impact on global warming; (4) minimal negative on-site impacts; (5) minimal negative off-site impacts; (6) minimal risk and maximum resilience; (7) preservation of biodiversity in agriculture; (8) preservation of biodiversity in nature and; (9) positive social outcomes. We focus on four technologies and production systems emerging in Australian agriculture: climate risk management; precision agriculture; crop–livestock integration and deficit irrigation. For each of these systems we identify how well they are likely to match the nine desirable attributes of an EIA system. While it seems unlikely that any single technology can satisfy all nine desirable attributes, there is hope that in combination emerging and future technologies will progress Australian agriculture towards greater productivity and ecological efficiency.
Water-use efficiency (WUE) is defined here as the ratio of grain yield (kg/ha) to crop water use by evapotranspiration (mm). Much of the WUE literature has focussed on either the determination of the ...boundary of attainable WUE for any amount of available water, or on the practicalities of measurement of the WUE of a crop. While these are important issues for defining the gap between the attained and the potential WUE, little progress has been reported on clarifying the components that contribute to this gap or on how it can be bridged. To address these questions, we analysed 334 wheat fields for which we had the data necessary to both calculate WUE and to simulate crop growth and water use. Simulations were conducted through Yield Prophet®, an on-line version of the APSIM systems model. For this dataset, evapotranspiration accounted for 69% of observed yield variation, although the more commonly used growing-season (April-October) rainfall accounted for 50%. Considering that evapotranspiration efficiency does not account for a wide range of potentially yield-limiting factors including soil and fertiliser nitrogen supply, crop phenology, and sowing dates, or rainfall distribution, these results reinforce the importance of evapotranspiration efficiency as a yield determinant for well managed crops in water-limited environments. WUE attained over the whole dataset was 15.2kggrain/ha.mm (x-intercept=67mm), although this value contained data subsets with important differences in WUE based on soil water-holding capacity and regional diversity. Yield Prophet® simulated commercial wheat yields with RMSDs of 0.80t/ha (r²=0.71), with some systematic error between observed and simulated yields. Simulated crops achieved a higher WUE (16.9kggrain/ha.mm; x-intercept=72mm) than the observed crops, probably because APSIM does not account for effects of factors such as weeds, pests and diseases and impacts of severe weather. Simulated 'what-if' analysis suggested that further improvement in WUE may be achieved with an early sowing strategy or a higher nitrogen input strategy. A 'yield maximising' strategy that included an optimal plant density, early sowing date, and higher nitrogen inputs resulted in an average WUE (21.4kggrain/ha.mm; x-intercept=80mm) that is close to the previously reported (French-Schultz) boundary of WUE. This outcome suggests a great deal of scope for Australian wheat growers to adopt strategies that improve their WUE. Yield Prophet® farmers have already demonstrated significant improvement in on-farm WUE compared with previous studies. However, additional improvements will only be partially realised due to considerations of the cost: benefit ratio and risk in a highly variable climate, and the operational feasibility of these strategies with current technologies.
Australia has a role to play in future global food security as it contributes 0·12 of global wheat exports. How much more can it contribute with current technology and varieties? The present paper ...seeks to quantify the gap between water-limited yield potential (Yw) and farmer yields (Ya) for wheat in Australia by implementing a new protocol developed by the Global Yield Gap and Water Productivity Atlas (GYGA) project. Results of past Australian yield gap studies are difficult to compare with studies in other countries because they were conducted using a variety of methods and at a range of scales. The GYGA project protocols were designed to facilitate comparisons among countries through the application of a consistent yet flexible methodology. This is the first implementation of GYGA protocols in a country with the high spatial and temporal climatic variability that exists in Australia. The present paper describes the application of the GYGA protocol to the whole Australian grain zone to derive estimates of rainfed wheat yield gap. The Australian grain zone was partitioned into six key agro-climatic zones (CZs) defined by the GYGA Extrapolation Domain (GYGA-ED) zonation scheme. A total of 22 Reference Weather Stations (RWS) were selected, distributed among the CZs to represent the entire Australian grain zone. The Agricultural Production Systems sIMulator (APSIM) Wheat crop model was used to simulate Yw of wheat crops for major soil types at each RWS from 1996 to 2010. Wheat varieties, agronomy and distribution of wheat cropping were held constant over the 15-year period. Locally representative dominant soils were selected for each RWS and generic sowing rules were specified based on local expertise. Actual yield (Ya) data were sourced from national agricultural data sets. To upscale Ya and Yw values from RWS to CZs and then to national scale, values were weighted according to the area of winter cereal cropping within RWS buffer zones. The national yield gap (Yg = Yw–Ya) and relative yield (Y% = 100 × Ya/Yw) were then calculated from the weighted values. The present study found that the national Yg was 2·0 tonnes (t)/ha and Y% was 47%. The analysis was extended to consider factors contributing to the yield gap. It was revealed that the RWS 15-year average Ya and Yw were strongly correlated (R
2 = 0·76) and that RWS with higher Yw had higher Yg. Despite variable seasonal conditions, Y% was relatively stable over the 15 years. For the 22 RWS, average Yg correlated positively and strongly with average annual rainfall amount, but surprisingly it correlated poorly with RWS rainfall variability. Similarly, Y% correlated negatively but less strongly (R
2 = 0·33) with RWS average annual rainfall, and correlated poorly with RWS rainfall variability, which raises questions about how Australian farmers manage climate risk. Interestingly a negative relationship was found between Yg and variability of Yw for the 22 RWS (R
2 = 0·66), and a positive relationship between Y% and Yw variability (R
2 = 0·23), which suggests that farmers in lower yielding, more variable sites are achieving yields closer to Yw. The Yg estimates appear to be quite robust in the context of estimates from other Australian studies, adding confidence to the validity of the GYGA protocol. Closing the national yield gap so that Ya is 0·80 of Yw, which is the level of Yg closure achieved consistently by the most progressive Australian farmers, would increase the average annual wheat production (20·9 million t in 1996/07 to 2010/11) by an estimated 15·3 million t, which is a 72% increase. This indicates substantial potential for Australia to increase wheat production on existing farmland areas using currently available crop varieties and farming practices and thus make a substantial contribution to achieving future global food security.
Climate risk assessment in cropping is generally undertaken in a top-down approach using climate records while critical farmer experience is often not accounted for. In the present study, set in ...south India, farmer experience of climate risk is integrated in a bottom-up participatory approach with climate data analysis. Crop calendars are used as a boundary object to identify and rank climate and weather risks faced by smallhold farmers. A semi-structured survey was conducted with experienced farmers whose income is predominantly from farming. Interviews were based on a crop calendar to indicate the timing of key weather and climate risks. The simple definition of risk as consequence × likelihood was used to establish the impact on yield as consequence and chance of occurrence in a 10-year period as likelihood. Farmers’ risk experience matches well with climate records and risk analysis. Farmers’ rankings of ‘good’ and ‘poor’ seasons also matched up well with their independently reported yield data. On average, a ‘good’ season yield was 1·5–1·65 times higher than a ‘poor’ season. The main risks for paddy rice were excess rains at harvesting and flowering and deficit rains at transplanting. For cotton, farmers identified excess rain at harvest, delayed rains at sowing and excess rain at flowering stages as events that impacted crop yield and quality. The risk assessment elicited from farmers complements climate analysis and provides some indication of thresholds for studies on climate change and seasonal forecasts. The methods and analysis presented in the present study provide an experiential bottom-up perspective and a methodology on farming in a risky rainfed climate. The methods developed in the present study provide a model for end-user engagement by meteorological agencies that strive to better target their climate information delivery.
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.
The Agricultural Production Systems Simulator (APSIM) is a modular modelling framework that has been developed by the Agricultural Production Systems Research Unit in Australia. APSIM was developed ...to simulate biophysical process in farming systems, in particular where there is interest in the economic and ecological outcomes of management practice in the face of climatic risk. The paper outlines APSIM's structure and provides details of the concepts behind the different plant, soil and management modules. These modules include a diverse range of crops, pastures and trees, soil processes including water balance, N and P transformations, soil pH, erosion and a full range of management controls. Reports of APSIM testing in a diverse range of systems and environments are summarised. An example of model performance in a long-term cropping systems trial is provided. APSIM has been used in a broad range of applications, including support for on-farm decision making, farming systems design for production or resource management objectives, assessment of the value of seasonal climate forecasting, analysis of supply chain issues in agribusiness activities, development of waste management guidelines, risk assessment for government policy making and as a guide to research and education activity. An extensive citation list for these model testing and application studies is provided.
PDF
