Australian Wheat Yield Research Articles

Increasing dominance of Indian Ocean variability impacts Australian wheat yields.

The relationships between crop productivity and climate variability drivers are often assumed to be stationary over time. However, this may not be true in a warming climate. Here we use a crop model and a machine learning algorithm to demonstrate the changing impacts of climate drivers on wheat productivity in Australia. We find that, from the end of the nineteenth century to the 1980s, wheat productivity was mainly subject to the impacts of the El Niño Southern Oscillation. Since the 1990s, the impacts from the El Niño Southern Oscillation have been decreasing, but those from the Indian Ocean Dipole have been increasing. The warming climate has brought more occurrences of positive Indian Ocean Dipole events, resulting in severe yield reductions in recent decades. Our findings highlight the need to adapt seasonal forecasting to the changing impacts of climate variability to inform the management of climate-induced yield losses.

Lead time and skill of Australian wheat yield forecasts based on ENSO-analogue or GCM-derived seasonal climate forecasts – A comparative analysis

Foresight of crop yield is fundamental to producers and industry to better manage climate risks and mitigate ebbs and troughs in crop production. Rain-fed grain production in Australia is highly volatile and producers and industry are progressively confronted with projected uncertainties due to climate variability and change, input costs and market prices. Thus, having advance knowledge of the likely impact of the coming season's climate on crop yield and production is critical for decisions across the supply chain. Here we explore and analyse the lead time and skill of a wheat yield forecasting system using a biophysical crop yield simulation model connected to either a statistical ENSO-analogue climate forecasting system or a dynamic general circulation model (GCM) derived climate forecasting system. The comparative skill was investigated for 16 wheat producing districts (shires) of the broad Australian winter cropping region, each containing 9–35 irregularly-spaced simulation points associated with climate stations. Both the ENSO-analogue and GCM-derived systems produced reliable wheat yield forecasts with the GCM-based approach having general improved skill, and particularly during the early months of the season (March to May) before sowing. The shift in the forecast yield distributions relative to the climatology-based yield distribution were dependent on location and time in the season, with the GCM-derived forecast shifts more widespread and earlier in the season. Overall, the GCM-based climate/crop forecasting system showed a significant improvement in lead time (greater than two months before the normal planting time of wheat), across the Australian wheat belt. This result demonstrates an avenue for improved efficacy in future commodity forecasting frameworks via likely enhanced relevance and utility to industry associated with the use of GCM-derived approaches.

Data fusion using climatology and seasonal climate forecasts improves estimates of Australian national wheat yields

National and regional yield forecasts can provide important insights into agribusiness beyond the farm gate. The incorporation of dynamical climate models into these forecasting systems strengthen their predictive performance in many cases but may contribute inherent biases to the final yield estimates. Downscaling the native climate model output so that is suitable for crop simulation modelling can also present challenges in representing realistic conditions for plant growth from a climate model. This study evaluated the performance of an operational national wheat yield forecast system for the Australian wheatbelt using climatology and seasonal climate model-based input data, and introduces an alternative approach using a data fusion method. The crop forecasting system uses the APSIM wheat model to estimate water-limited potential yield. The climatology-based forecast tended to over predict national yield (high yield bias; 1.5 to 7% across forecast months), while the model-based method (using ACCESS-S1 dynamical model) tended to under predict yield (low yield bias; -5.9 to -0.5% across forecast months) and had a lower spread than climatology (10 to 50% lower across forecast months). The model-based forecast had skill in terms of accuracy and reliability during the second half of the season. The newly developed data fusion method used a weighting method calibrated for separate forecast locations (stations) to remove bias in the mean forecast yield and reduce ensemble spread. This resulted in improvements in the Australia-wide yield forecasts across all forecast start dates. This study provides a demonstration of how a data-driven approach can be applied to a crop forecast to improve accuracy and resolution of crop yield forecasts without the need for more computationally intensive downscaling approaches.

Climate drivers provide valuable insights into late season prediction of Australian wheat yield

Foresight of grain yields prior to harvest would be empowering for many stakeholders along the supply chain from farmers through to bulk handlers, banks and insurance companies. Estimating Australian grain production ahead of harvest is difficult for many reasons including the highly variable year to year rainfall. The rainfall in the final months prior to harvest, can be crucial to final harvest totals. Here we explore the importance of rainfall from September 1, which broadly corresponds to the close of the top-dressing fertilizer application window, for the remaining cropping season in determining final yield. This is assessed via sensitivity analysis of water-limited wheat potential yield totals from historical climate in the APSIM crop model. At locations where the rainfall influences wheat yield, we compare three methods to forecast wheat yields that differ based on the climate data input: 1) climatology approach, which uses 30 years of observed climate data, 2) analogue climatology, which uses information from climate drivers (El-Niño Southern Oscillation and Indian Ocean Dipole) to create analogue years; and 3) dynamical climate forecasts from a general circulation model (ACCESS-S). We find that potential yields strongly depend on in-season plant available water (PAW) where years with high PAW are unaffected by the late season rainfall. Predicting the potential yield from analogue climatology (climate drivers) had the greatest skill, with smallest Root Mean Squared Error of 0.45 t/ha. This approach ranked first for 42% of the study locations compared to the climatology and ACCESS-S forecasting methods. This knowledge can help inform decision makers about the need to incorporate seasonal climate forecasts and the most appropriate climate forecasting method.

Early sowing systems can boost Australian wheat yields despite recent climate change

Price surges in staple foods trigger civil unrest and conflict1. The food riots of 2007–2008 and Arab spring uprisings (2010–2012) were, in part, a consequence of price increases due to a tightening supply of staple grains, particularly wheat. Prolonged drought in Australia contributed to the global wheat shortage; Australia accounts for 10% of global wheat exports2. Australian wheat yields have plateaued3 owing to reduced rainfall4,5 and increasing temperatures3 attributed to anthropogenic climate change6. If Australia is to increase wheat production in line with projected global population growth and demand, an increase in yield is required7. Crop simulations reveal that an early sowing system combined with slower-developing wheat genotypes could exploit a longer growing season8. We developed near-isogenic lines and tested this hypothesis in experiments across the grain belt of Australia, and extended the results using whole-farm simulations. Our proposed early sowing system can increase national yields by 0.54 (s.d. = 0.38) t ha−1 representing an additional 7.1 Mt annually under reduced rainfall and increasing temperature regimes. This adaptation could facilitate increasing yields across Australia under climate change with global food security benefits. Crop models suggest that early sowing and slower-developing cultivars could maintain Australian wheat yields despite less-favourable climatic conditions. Field trials now confirm the potential of this adaptation for wheat production across Australia.

Modeling the joint influence of multiple synoptic-scale, climate mode indices on Australian wheat yield using a vine copula-based approach

Abstract Twelve large-scale climate drivers are employed to investigate their spatio-temporal influence on the variability of seasonal wheat yield in five major wheat-producing states across Australia using data for the period 1983–2013. Generally, the fluctuations in the Indian Ocean appear to have a dominant effect on the Australian wheat crop in all states except Western Australia, while the impact of oceanic conditions in the Pacific region is much stronger in Queensland. The results show a statistically significant negative correlation between the Indian Ocean Dipole (IOD) and the anomalous wheat yield in the early growing stage of the crop in the eastern and southeastern wheat belt regions. This correlation suggests that the wheat yield can be skillfully forecast 3–6 months ahead, supporting early decision-making in regard to precision agriculture. In this study, we use vine copula models to capture climate-yield dependence structures, including the occurrence of extreme events (i.e., the tail dependences). The co-occurrence of extreme events is likely to enhance the impacts of climate mode and this can be quantified probabilistically through conditional copula-based models. Generally, the developed D-vine quantile regression model provide greater accuracy for the forecasting of wheat yield at given different confidence levels compared to the traditional linear quantile regression (LQR) method. A five-fold cross-validation approach is also used to estimate the out-of-sample accuracy of both copula-statistical forecasting models. These findings provide a comprehensive analysis of the spatio-temporal impacts of different climate mode indices on Australian wheat crops. Improved quantification of the impacts of large-scale climate drivers on the wheat yield can allow a development of suitable planning processes and crop production strategies designed to optimize the yield and agricultural profit.

Yield improvement and adaptation of wheat to water-limited environments in Australia—a case study

The improvement in grain yield of wheat throughout Australia through both breeding and management has been impressive. Averaged across all farms, there has been an approximate doubling of yield per unit area since ~1940. This has occurred across a broad range of environments with different rainfall patterns. Interestingly, the gain in the driest years (9 kg ha–1 year–1 or 0.81% year–1) has been proportionally greater than in the most favourable years (13.2 kg ha–1 year–1 or 0.61% per year) when expressed as yield relative to 2012. These data from all farms suggest that further yield progress is likely, and evidence is presented that improved management practices alone could double this rate of progress. The yield increases achieved have been without any known compromise in grain quality or disease resistance. As expected, improvements have come from both changed management and from better genetics, as well as from the synergy between them. Yield improvements due to changed management have been dramatic and are easiest to quantify, whereas those from breeding have been important but more subtle. The management practices responsible have largely been driven by advances in mechanisation that enable direct seeding, more timely and flexible sowing and nutrient management, and improved weed and pest control, many of which have been facilitated by improved crop sequences with grain legumes and oilseeds that improve water- and nutrient-use efficiency. Most of the yield improvements from breeding in Australia have come from conventional breeding approaches where selection is almost solely for grain yield (together with grain quality and disease resistance). Improvements have primarily been through increased harvest index (HI), although aboveground biomass has also been important. We discuss future opportunities to further increase Australian rainfed wheat yields. An important one is earlier planting, which increases resource capture. This will require knowledge of the genes regulating phenological development so that flowering still occurs at the optimum time; appropriate modifications to sowing arrangements and nutrient management will also be required. To improve yield potential, we propose a focus on physiological traits that increase biomass and HI and suggest that there may be more scope to improve biomass than HI. In addition, there are likely to be important opportunities to combine novel management practices with new breeding traits to capture the synergy possible from variety × management interactions. Finally, we comment on research aimed at adapting agriculture to climate change.

Potential impact of climate change on wheat yield in South Australia

Refined and improved climate change scenarios have been applied in this study to quantify the possible impacts of future climate change on South Australian wheat yield with probability attached. This study used the APSIM-Wheat module and information drawn from the Special Report on Emission Scenarios (SRES) and nine climate models for 2080. A wheat yield response surface has been constructed within 80 climate change scenarios. The most likely wheat yield changes have been defined under combinations of changes in regional rainfall, regional temperature and atmospheric CO 2 concentration (CO 2). Median grain yield is projected to decrease across all locations from 13.5 to 32% under the most likely climate change scenarios. This has economic and social implications from local to national levels.

Three Putative Types of El Niño Revealed by Spatial Variability in Impact on Australian Wheat Yield

Abstract The El Niño–Southern Oscillation (ENSO) phenomenon significantly impacts rainfall and ensuing crop yields in many parts of the world. In Australia, El Niño events are often associated with severe drought conditions. However, El Niño events differ spatially and temporally in their manifestations and impacts, reducing the relevance of ENSO-based seasonal forecasts. In this analysis, three putative types of El Niño are identified among the 24 occurrences since the beginning of the twentieth century. The three types are based on coherent spatial patterns (“footprints”) found in the El Niño impact on Australian wheat yield. This bioindicator reveals aligned spatial patterns in rainfall anomalies, indicating linkage to atmospheric drivers. Analysis of the associated ocean–atmosphere dynamics identifies three types of El Niño differing in the timing of onset and location of major ocean temperature and atmospheric pressure anomalies. Potential causal mechanisms associated with these differences in anomaly patterns need to be investigated further using the increasing capabilities of general circulation models. Any improved predictability would be extremely valuable in forecasting effects of individual El Niño events on agricultural systems.

Spatial and temporal patterns in Australian wheat yield and their relationship with ENSO

Spatial and temporal variability in wheat production in Australia is dominated by rainfall occurrence. The length of historical production records is inadequate, however, to analyse spatial and temporal patterns conclusively. In this study we used modelling and simulation to identify key spatial patterns in Australian wheat yield, identify groups of years in the historical record in which spatial patterns were similar, and examine association of those wheat yield year groups with indicators of the El Niño Southern Oscillation (ENSO). A simple stress index model was trained on 19 years of Australian Bureau of Statistics shire yield data (1975–93). The model was then used to simulate shire yield from 1901 to 1999 for all wheat-producing shires. Principal components analysis was used to determine the dominating spatial relationships in wheat yield among shires. Six major components of spatial variability were found. Five of these represented near spatially independent zones across the Australian wheatbelt that demonstrated coherent temporal (annual) variability in wheat yield. A second orthogonal component was required to explain the temporal variation in New South Wales. The principal component scores were used to identify high- and low-yielding years in each zone. Year type groupings identified in this way were tested for association with indicators of ENSO. Significant associations were found for all zones in the Australian wheatbelt. Associations were as strong or stronger when ENSO indicators preceding the wheat season (April–May phases of the Southern Oscillation Index) were used rather than indicators based on classification during the wheat season. Although this association suggests an obvious role for seasonal climate forecasting in national wheat crop forecasting, the discriminatory power of the ENSO indicators, although significant, was not strong. By examining the historical years forming the wheat yield analog sets within each zone, it may be possible to identify novel climate system or ocean–atmosphere features that may be causal and, hence, most useful in improving seasonal forecasting schemes.

Climate change and Australian wheat yield

Nicholls1 reported that 30-50% of the increase in Australian wheat yields in the period 1952-92 resulted from climate change. He estimated a simple linear relationship where a 1°C fall in diurnal temperature range increased Australian wheat yield by 0.52 t ha−1. This effect, taken with the trend in diurnal range, accounted for 45% of the yield increase between 1952 and 1992. In an extended model with three climate variables, Nicholls found that changes in minimum temperature had had most impact on wheat yield and that rainfall change had contributed little. In our view, Nicholls's results need qualification and should be interpreted with caution. They are only estimates, and do not include standard errors to indicate their precision.

Climate change and Australian wheat yield

We believe that Nicholls's conclusion1 — that reduced frost frequency has been predominantly responsible for 30-50% of the 45% increase in average Australian wheat yield between 1952 and 1992 — is not correct.

Climate change and Australian wheat yield

Nicholls replies — Godden state that I proxied non-climate factors “by a linear trend in yields”. I did not and my conclusions were not dependent on the functional form of any relationship between non-climate factors and yield. They suggest that forcing the intercept to zero and fitting with ordinary least-squares biases my estimates. I repeated the analysis without forcing a zero intercept, with no significant effect on the results. The climate-yield change relationship is so strong (see Fig. 2 of Ref. 1) that other forms of fitting produce a result very similar to ordinary least-squares. This relationship dominates the effects of other variables and provides a way to estimate the effect of climate on yield change. Godden et al. believe that detrending with first differences is inappropriate. As I noted, repeating the analysis on residuals from fitted non-linear trends produces very similar results.

Increased Australian wheat yield due to recent climate trends

The possibility that future climate change may affect agriculture has attracted considerable attention1,2. As a step towards evaluating such influences, the effect of climate trends over the past few decades3 needs to be assessed. Here I estimate the contribution of climate trends in Australia4,5 to the substantial increase in Australian wheat yields since 1952. Non-climatic influences— such as new cultivars and changes in crop management practices—are removed by detrending the wheat yield and climate variables and using the residuals to calculate quantitative relationships between variations in climate and yield. Climate trends appear to be responsible for 30–50% of the observed increase in wheat yields, with increases in minimum temperatures being the dominant influence. This approach should be applicable in other regions for which sufficient data exist.

Forecasting the favourableness of the Australian environment for the Russian wheat aphid, Diuraphis noxia (Homoptera: Aphididae), and its potential impact on Australian wheat yields

AbstractThe CLIMEX model for matching climates in ecology is used to forecast the favourableness of the Australian environment for Diuraphis noxia (Mordvilko). Parameters used in CLIMEX for D. noxia were derived from the literature and tuned by iterative matching with the known distribution of the aphid in North America. These fitted values were then validated using the known distribution of D. noxia in southern Africa, and with the country-by-country occurrence of the aphid around the world. The results suggest that the drier inland parts of the Australian wheat belt would be very favourable for D. noxia growth and survival. A computer model of the interactions between climate, wheat growth and the biology of D. noxia was then used to simulate the likely infestation rates and potential yield reductions at selected sites in both the summer and winter rainfall regions of the Australian wheat belt. The results indicate that D. noxia has potential to cause severe losses to the wheat crop should it arrive in Australia.

Australian Rainfall and Wheat Yield

UNTIL large schemes for the conservation of water supply with a view to irrigation have been carried out, the incidence of drought at frequent intervals is bound to have a great influence, not only on the sheep runs of the Australian Commonwealth, but also on its wheat crop. It is perhaps surprising that the relation between rainfall and wheat yield should be to a great extent directly traceable, when we consider to how many indirect influences the yield is exposed. The seed varies in such obvious characteristics as size and hardness, as well as in power of resistance to disease, partly modified by the conditions under which the crop producing the seed has been raised. There is, moreover, no constancy in the soil, which differs from place to place in composition, in aspect, elevation, and slope, from farm to farm in the amount and choice of fertilising agents, and from district to district in the dates of weather changes and precipitation. There is possible loss by barrenness of seed, by ground pests before germination, by vermin during growth, by storms, birds, insects, and disease when the grain is in the ear, and much may be shaken out when ripe if the harvest oweather be very hot and dry.