Transformational agronomy by growing summer crops in winter: The cropping system and farm profits

The idea that “Yield is King” fails to acknowledge that what matters most to farmers is farm profits and risk, rather than yield. This is because decisions made in one season will affect options and crop performance over the next few years. Therefore, quantifying the longer-term impacts of innovation adoption is important. We used the Agricultural Production Simulation model (APSIM) to simulate and investigate the implications of adopting rain-fed winter sown sorghum in the Australian northern grains region. Results indicate that within a crop rotation early-planted sorghum will tend to decrease median sorghum crop yields but increase the following winter crop yields. This appears to have a marginal economic effect in Breeza and Dalby but encouraging results in Emerald. The inclusion of chickpea within the rotation increased returns in the best seasons with little change to downside risks in poor seasons

Assessing linear interpolation to generate daily radiation and temperature data for use in crop simulations

In this study we compare simulated, water non-limited yields obtained from the use of actual daily radiation and temperature data against yields simulated with weather data estimated using linear interpolation (LI) from monthly means. Using LI-generated radiation and temperature data (instead of stochastically generated or actual data) greatly reduces the effort required for the simulation task. LI data do not have the day-to-day variations that occur naturally. This paper determines under what condition crop model yields are insensitive to this day-to-day variation. Four locations across Iran with different climates were selected for the study. For each location a complete set of 20-year daily radiation and temperature data were available. Another 20-year sets of daily data were produced by LI from monthly means values. For the assessment we used the wheat, maize and soybean models of Decision Support System for Agrotechnology Transfer (DSSAT) at several sowing dates. Generally, simulated crop yields using actual and LI weather data showed similar responses to sowing date. Across crop models and locations, mean, variance and distribution differences between yields simulated with actual and LI data were significant in 23, 24 and 22% of cases, respectively. In many cases, the lack of day-to-day variation in LI data and hence its inability to reproduce extreme temperatures (cold or hot events) caused these significant differences, especially at unseasonally early or late sowing dates. In other cases, over-prediction of biomass production with LI data resulted in an over-prediction of yield. However, this growth over-prediction is only significant where growing season air temperatures are optimal or supra optimal for growth and have a high day-to-day variation (standard deviation greater than 3.5 °C). We concluded that for conventional sowing dates and in situations where unseasonally high or low temperatures are either unlikely or of little importance, the LI method could be used to generate daily radiation and temperature data as input for crop simulations

A simple regional-scale model for forecasting sorghum yield across North-Eastern Australia

Sorghum is the main dryland summer crop in NE Australia and a number of agricultural businesses would benefit from an ability to forecast production likelihood at regional scale. In this study we sought to develop a simple agro-climatic modelling approach for predicting shire (statistical local area) sorghum yield. Actual shire yield data, available for the period 1983-1997 from the Australian Bureau of Statistics, were used to train the model. Shire yield was related to a water stress index (SI) that was derived from the agro-climatic model. The model involved a simple fallow and crop water balance that was driven by climate data available at recording stations within each shire. Parameters defining the soil water holding capacity, maximum number of sowings (MXNS) in any year, planting rainfall requirement, and critical period for stress during the crop cycle were optimised as part of the model fitting procedure. Cross-validated correlations (CVR) ranged from 0.5 to 0.9 at shire scale. When aggregated to regional and national scales, 78-84% of the annual variation in sorghum yield was explained. The model was used to examine trends in sorghum productivity and the approach to using it in an operational forecasting system was outlined. (c) 2005 Elsevier B.V. All rights reserved

Simulation of whole farm management decisions

The simulation model APSFarm is an extended configuration of the systems model APSIM, and has been in development since 2005 to support the analysis of farm case studies in environments ranging from Central Queensland to the Victorian Riverina. One of the major extensions to APSIM was the implementation of dynamic farm management as a set of state transition networks, with each network representing the operation of a particular paddock or management unit. Each paddock has a current state (eg. fallow, crop), “rules” that allow transition to adjacent states, and “actions” that are taken when such a transition is made. These rules represent feasibility (eg. whether it is the correct planting season for this particular crop, whether machinery is available), tactics (eg fertiliser management), and strategy (eg. crop sequencing and mix of enterprise). Each day, the model examines all paths leading away from the current state to adjacent states. Should the mathematical product of all rules associated with a path be non-zero, the path becomes a candidate for action. Should more than one candidate be present, the highest ranking path is chosen, and its associated management actions to change state are undertaken (eg. sowing or harvesting a crop). There may be cascading events that flow from a state change, so the process is repeated until nothing more can be done for that day. The benefits of the network approach are multifold; gross patterns can be identified from the network structure that allows comparison between farmers and farm types. From a software perspective, details of the transition rules and their associated actions are readily accessible, instead of being encoded in complex logic code constructs. Encapsulating management strategy as a data structure instead of a series of specific instructions allows the construction of dynamic analysis tools in which complex decisions can be more clearly described as sets of simple and measurable rules of thumb. Further, this data structure can make the task of providing a cohesive user interface much easier, facilitating graphical representations of farm management. This paper presents a software tool used in studying diverse management systems drawn from ongoing case studies, discusses how the management systems are elucidated from farm managers and demonstrates some of the diagnostics available from such tools. © MODSIM 2009.All rights reserved

Predicting optimum crop designs using crop models and seasonal climate forecasts

Abstract Expected increases in food demand and the need to limit the incorporation of new lands into agriculture to curtail emissions, highlight the urgency to bridge productivity gaps, increase farmers profits and manage risks in dryland cropping. A way to bridge those gaps is to identify optimum combination of genetics (G), and agronomic managements (M) i.e. crop designs (GxM), for the prevailing and expected growing environment (E). Our understanding of crop stress physiology indicates that in hindsight, those optimum crop designs should be known, while the main problem is to predict relevant attributes of the E, at the time of sowing, so that optimum GxM combinations could be informed. Here we test our capacity to inform that “hindsight”, by linking a tested crop model (APSIM) with a skillful seasonal climate forecasting system, to answer “What is the value of the skill in seasonal climate forecasting, to inform crop designs?” Results showed that the GCM POAMA-2 was reliable and skillful, and that when linked with APSIM, optimum crop designs could be informed. We conclude that reliable and skillful GCMs that are easily interfaced with crop simulation models, can be used to inform optimum crop designs, increase farmers profits and reduce risks

To mulch or to munch? Big modelling of big data

African farmers are poorly resourced, highly diverse and aground by poverty traps making them rather impervious to change. As a consequence R4D efforts usually result in benefits but also trade-offs that constraint adoption and change. A typical case is the use of crop residues as mulches or as feedstock. Here we linked a database of household surveys with a dynamic whole farm simulation model, to quantify the diversity of trade-offs from the alternative use of crop residues. Simulating all the households in the survey (n = 613) over 99 years of synthetic climate data, showed that benefits and trade-offs from “mulching or munching” differ across agro-ecologies, and within agro-ecologies across typologies of households. Even though trade-offs between household production or income and environmental outcomes could be managed; the magnitude of the simulated benefits from the sustainable intensification of maize-livestock systems were small. Our modelling framework shows the benefits from the integration of socio-economic and biophysical approaches to support the design of development programs. Our results support the argument that a greater focus is required on the development and diversification of farmers' livelihoods within the framework of an improved understanding of the interconnectedness between biophysical, socio-economic and market factors

To mulch or to munch?:big modelling of big data

African farmers are poorly resourced, highly diverse and aground by poverty traps making them rather impervious to change. As a consequence R4D efforts usually result in benefits but also trade-offs that constraint adoption and change. A typical case is the use of crop residues as mulches or as feedstock. Here we linked a database of household surveys with a dynamic whole farm simulation model, to quantify the diversity of trade-offs from the alternative use of crop residues. Simulating all the households in the survey (n = 613) over 99 years of synthetic climate data, showed that benefits and trade-offs from “mulching or munching” differ across agro-ecologies, and within agro-ecologies across typologies of households. Even though trade-offs between household production or income and environmental outcomes could be managed; the magnitude of the simulated benefits from the sustainable intensification of maize-livestock systems were small. Our modelling framework shows the benefits from the integration of socio-economic and biophysical approaches to support the design of development programs. Our results support the argument that a greater focus is required on the development and diversification of farmers' livelihoods within the framework of an improved understanding of the interconnectedness between biophysical, socio-economic and market factors

Understanding the diversity in yield potential and stability among commercial sorghum hybrids can inform crop designs

Reducing yield gaps in dryland cropping depends on our capacity to identify combinations of genetics (G) and management (M) (i.e. crop designs, G × M) that best suit site and seasonal conditions (the environment, E). We combined empirical and modelling approaches to characterise and explain the yield stability and yield potential of commercial sorghum hybrids when grown under a range of agronomic managements and environments yielding between 3 and 12 t ha. The empirical data includes two seasons (2014-15 and 2015–2016) of on-farm and on-research station trials conducted across six sites in Queensland, Australia. Agronomic management treatments included plant density, row configuration, level of irrigation and fertiliser inputs, and time of sowing. Six hybrids contrasting in maturity and tillering type were characterised relative to the industry standard MR-Buster in terms of yield potential, yield stability (b), and an expected utility index that combines both indices. A medium-late maturity and high tillering hybrid (MR-Scorpio), had the highest utility rank and showed high b values due to high tiller productivity. A variety of significant row spacing and configuration, and plant density effects on yield were observed, but these were inconsistent across sites and seasons. A long-term simulation experiment across contrasting environments was used to identify hybrid traits and managements capable of modifying yield stability. Combined with the empirical data, the simulations suggest hybrids showing high biomass production and multiple productive tillers can increase the response of yield to the productivity of the environment, whereas reducing the thermal time to floral initiation may increase the stability of yields across environments. Expected changes in hybrid rank due to such G × E interactions, along with the complex effects of management on yield, increase the need to match crop design to specific sites and seasons. The value of targeted crop design depends on the diversity of traits among commercial hybrids and the availability of a skilful seasonal climate forecast to allow farmers to match hybrids and management to prevailing and expected seasonal conditions