Water-limited Yield Research Articles

Assessment of water-limited winter wheat yield potential at spatially contrasting sites in Ireland using a simple growth and development model

AbstractAlthough Irish winter wheat yields are among the highest globally, increases in the profitability of this crop are required to maintain its economic viability. However, in order to determine if efforts to further increase Irish wheat yields are likely to be successful, an accurate estimation of the yield potential is required for different regions within Ireland. A winter wheat yield potential model (WWYPM) was developed, which estimates the maximum water-limited yield achievable, within the confines of current genetic resources and technologies, using parameters for winter wheat growth and development observed recently in Ireland and a minor amount of daily meteorological input (maximum and minimum daily temperature, total daily rainfall and total daily incident radiation). The WWYPM is composed of three processes: (i) an estimation of potential green area index, (ii) an estimation of light interception and biomass accumulation and (iii) an estimation of biomass partitioning to grain yield. Model validation indicated that WWYPM estimations of water-limited yield potential (YPw) were significantly related to maximum yields recorded in variety evaluation trials as well as regional average and maximum farm yields, reflecting the model’s sensitivity to alterations in the climatic environment with spatial and seasonal variations. Simulations of YPw for long-term average weather data at 12 sites located at spatially contrasting regions of Ireland indicated that the typical YPw varied between 15.6 and 17.9 t/ha, with a mean of 16.7 t/ha at 15% moisture content. These results indicate that the majority of sites in Ireland have the potential to grow high-yielding crops of winter wheat when the effects of very high rainfall and other stresses such as disease incidence and nutrient deficits are not considered.

Modelling wheat yield change under CO2 increase, heat and water stress in relation to plant available water capacity in eastern Australia

Abstract Increasing heat and water stress are important threats to wheat growth in rain-fed conditions. Using climate scenario-based projections from the Coupled Model Intercomparison Project phase 5 (CMIP5), we analysed changes in the probability of heat stress around wheat flowering and relative yield loss due to water stress at six locations in eastern Australia. As a consequence of warmer average temperatures, wheat flowering occurred earlier, but the probability of heat stress around flowering still increased by about 3.8%–6.2%. Simulated potential yield across six sites increased on average by about 2.5% regardless of the emission scenario. However, simulated water-limited yield tended to decline at wet and cool locations under future climate while increased at warm and dry locations. Soils with higher plant available water capacity (PAWC) showed a lower response of water-limited yield to rainfall changes except at very dry sites, which means soils with high PAWC were less affected by rainfall changes compared with soils with low PAWC. Our results also indicated that a drought stress index decreased with increasing PAWC and then stagnated at high PAWC. Under high emission scenario RCP8.5, drought stress was expected to decline or stay about the same due to elevated CO 2 compensation effect. Therefore, to maintain or increase yield potential in response to the projected climate change, increasing cultivar tolerance to heat stress and improving crop management to reduce impacts of water stress on lower plant available water holding soils should be a priority for the genetic improvement of wheat in eastern Australia.

Water use and environmental parameters influence proso millet yield

Proso millet (Panicum miliaceum L.) is a short-season, drought-tolerant C4 species capable of making use of limited available water supplies and is suitable for dryland crop rotations in the Central Great Plains. Previously published water use/yield production functions for proso millet have slopes lower than reported for other C4 species in this region. The objectives of this experiment were to determine the water-limited yield relationship for proso millet and to identify environmental factors that cause yields to be lower than predicted by the water-limited yield relationship. Water use and yield data were obtained from a long-term crop rotation experiment conducted under dryland conditions in northeast Colorado from 1995 to 2016. Stepwise linear regression analysis was used to determine important environmental factors influencing yield. The water-limited yield relationship had a slope consistent with other C4 species in this region (32.57kgha−1 per mm of water use). A relationship based on growing season water use, plant available soil water at planting, precipitation received from 12 to 18 August, number days in July and August with maximum temperature greater than 36°C, daily average wind run and maximum wind gust during the week before swathing explained 88% of yield variability. The results of this analysis suggest that closing the yield gap for proso millet production could likely result from efforts to breed for enhanced shattering resistance and heat tolerance and from production methods that improve precipitation storage efficiency during the non-crop period prior to millet planting and increase available soil water at millet planting.

Soybean yield in future climate scenarios for the state of Rio Grande do Sul, Brazil

Abstract: The objective of this work was to estimate the yield potential and the water-limited yield of soybean (Glycine max) in the state of Rio Grande do Sul, Brazil, in two future climate scenarios, SRES A1B and RCP4.5, using the SoySim and Cropgro-Soybean simulation models. In both models, three maturity groups (4.8, 5.5, and 6.0) and six sowing dates (09/01, 10/01, 11/01, 12/01, 01/01, and 02/01) were considered in the SRES A1B-CMIP3 and RCP4.5-CMIP5 scenarios. The analyzed variable was grain yield at 13% moisture (Mg ha-1). Soybean yield potential in Rio Grande do Sul should increase up to the end of the 21st century, according to both scenarios. Water-limited yield of soybean also increases up to the end of the 21st century, by the SRES A1B-CMIP3 scenario; however, it will decrease in future periods, by the RCP4.5-CMIP5 scenario because of limited soil water.

Estimating maize yield potential and yield gap with agro-climatic zones in China—Distinguish irrigated and rainfed conditions

Understanding yield potential (Yp) and yield gap (Yg) in current intensive maize (Zea mays L.) production is essential to meet future food demand with the limited resources. In this study, we used the agro-climatic zones (CZs) and the reference weather stations (RWS) buffer zones, together with the Hybird-Maize model to estimate maize Yp in the four maize-growing-regions of China under both irrigated and rainfed conditions. In irrigated maize areas, we got 70 RWS buffer zones, and total maize area in the RWS buffer zones covered 67% of the whole irrigated maize area. In rainfed maize areas, we got 106 RWS buffer zones, which covered 51% of the whole rainfed maize area. As a result, the average Yp was 14.2tha−1 and farmers have achieved 58% of Yp. The average water-limited yield potential (Yw) was 10.7tha−1 and farmers have achieved 65% of Yw. Further analysis for four maize-growing-regions showed that precipitation was a limiting factor for Yw to fully achieve Yp except in Southwest China (SW), whereas the average precipitation was more than 653mm during maize growing season. The ratio between Yw and Yp (Yw/Yp) was 51% in Northwest China (NW), and around 80% in both Northeast China (NE) and North China Plain (NCP). The comparison of Yp in different regions showed the low Yp in NE was due to low temperature while Yp in both NCP and SW were limited by low solar radiation. In conclusion, our findings highlight the efficiency and importance to estimate Yp, Yw and Yg by the upscaling method with CZs and RWS buffer zones. Meanwhile, the comparison of Yp, Yw and Yg in different regions was important to improve maize production in future in China.

Climate trends account for stalled wheat yields in Australia since 1990.

Global food security requires that grain yields continue to increase to 2050, yet yields have stalled in many developed countries. This disturbing trend has so far been only partially explained. Here, we show that wheat yields in Australia have stalled since 1990 and investigate the extent to which climate trends account for this observation. Based on simulation of 50 sites with quality weather data, that are representative of the agro-ecological zones and of soil types in the grain zone, we show that water-limited yield potential declined by 27% over a 26year period from 1990 to 2015. We attribute this decline to reduced rainfall and to rising temperatures while the positive effect of elevated atmospheric CO2 concentrations prevented a further 4% loss relative to 1990 yields. Closer investigation of three sites revealed the nature of the simulated response of water-limited yield to water availability, water stress and maximum temperatures. At all three sites, maximum temperature hastened time from sowing to flowering and to maturity and reduced grain number per m2 and average weight per grain. This 27% climate-driven decline in water-limited yield is not fully expressed in actual national yields. This is due to an unprecedented rate of technology-driven gains closing the gap between actual and water-limited potential yields by 25kgha-1 yr-1 enabling relative yields to increase from 39% in 1990 to 55% in 2015. It remains to be seen whether technology can continue to maintain current yields, letalone increase them to those required by 2050.

Yield gaps in oil palm: A quantitative review of contributing factors

Abstract Oil palm, currently the world’s main vegetable oil crop, is characterised by a large productivity and a long life span (≥25 years). Peak oil yields of 12 t ha−1 yr−1 have been achieved in small plantations, and maximum theoretical yields as calculated with simulation models are 18.5 t oil ha−1 yr−1, yet average productivity worldwide has stagnated around 3 t oil ha−1 yr−1. Considering the threat of expansion into valuable rainforests, it is important that the factors underlying these existing yield gaps are understood and, where feasible, addressed. In this review, we present an overview of the available data on yield-determining, yield-limiting, and yield-reducing factors in oil palm; the effects of these factors on yield, as measured in case studies or calculated using computer models; and the underlying plant-physiological mechanisms. We distinguish four production levels: the potential, water-limited, nutrient-limited, and the actual yield. The potential yield over a plantation lifetime is determined by incoming photosynthetically active radiation (PAR), temperature, atmospheric CO2 concentration and planting material, assuming optimum plantation establishment, planting density (120–150 palms per hectares), canopy management (30–60 leaves depending on palm age), pollination, and harvesting. Water-limited yields in environments with water deficits >400 mm year−1 can be less than one-third of the potential yield, depending on additional factors such as temperature, wind speed, soil texture, and soil depth. Nutrient-limited yields of less than 50% of the potential yield have been recorded when nitrogen or potassium were not applied. Actual yields are influenced by yield-reducing factors such as unsuitable ground vegetation, pests, and diseases, and may be close to zero in case of severe infestations. Smallholders face particular constraints such as the use of counterfeit seed and insufficient fertiliser application. Closing yield gaps in existing plantations could increase global production by 15–20 Mt oil yr−1, which would limit the drive for further area expansion at a global scale. To increase yields in existing and future plantations in a sustainable way, all production factors mentioned need to be understood and addressed.

Winter wheat cultivars in Australian farming systems: a review

Winter wheat cultivars are defined as those that have an obligate vernalisation requirement that must be met before they will progress from the vegetative to reproductive phase of development i.e. they must experience a true winter before they will flower. Historically, very little breeding effort has been applied to the selection of winter cultivars suited to southern Australia, with the notable exception of the New South Wales Agriculture breeding program based in Wagga and Temora that ran from the 1960s until 2002. A shift by growers to earlier sowing, increased usage of dual-purpose cereals, and research highlighting the whole-farm benefits of winter cultivars to average farm wheat yield has increased grower interest and demand for winter cultivars. Three major wheat breeding companies operating in southern Australia have responded by commencing selection for milling quality winter cultivars, the first of which was released in 2017. Existing research relating to winter wheats in southern Australian farming systems is reviewed here, including interactions with agronomic management, environment and weeds and disease. It is concluded that winter wheats can offer significant production and farming system benefits to growers by allowing earlier establishment, which increases water-limited potential yield (PYw) by ~15% relative to later sown spring wheats, and makes forage available for dual-purpose grazing during vegetative development. Winter wheats sown early require agronomic management different to that of later sown spring wheats, including greater attention to control of grass weeds and certain diseases. There are significant research gaps that will prevent growers from maximising the opportunities from new winter cultivars once they are released. The first of these is a well-defined establishment window for winter cultivars, particularly in medium-low rainfall environments of South Australia, Victoria and Western Australia that have not historically grown them. There is circumstantial evidence that the yield advantage of early established winter wheats over later sown spring wheats is greatest when stored soil water is present at establishment, or the soil profile fills during the growing season. Explicit confirmation of this would allow growers to identify situations where the yield advantage of winter wheats will be maximised. Given the imminent release of several new winter wheat cultivars and the increases in PYw that they embody, it is critical to experimentally define the management and environmental conditions under which performance of these new genotypes are optimised, before their release and availability to growers. Optimising the genotype × environmental × management interactions possible with these cultivars will empower growers to make the best use of the technology and better realise the gains in water limited potential yield possible with these genotypes.

Impact of climatic variables on the spatial and temporal variability of crop yield and biomass gap in Sub-Saharan Africa- a case study in Central Ghana

We investigated the impact of climate variables on yield and biomass gap variability in two humid topical regions, Brong-Ahafo and Ashanti region, of central Ghana using the crop model LINTUL5 embedded into a general modeling framework, SIMPLACE (Scientific Impact Assessment and Modelling Platform for Advanced Crop and Ecosystem Management). The simulations were run using a late maturity maize variety (Obatanpa) and historical weather data (1992–2007) across the 18 districts of the regions studied. The simulated maize yield and biomass production under water-limited conditions varied spatially which was significantly correlated with the solar radiation and precipitation in the crop growing period (R2=0.99; p<0.05), whereas, associated temporal variability in the simulated maize yield was significantly correlated with the radiation in the crop growing period (R2=0.96; p<0.05). The temporal variability in the simulated potential biomass production was significantly correlated with the solar radiation and average temperature in the crop growing period (R2=0.93; p<0.05), postulating that the solar radiation and the mean temperature are the limiting climatic factor in the study regions. Yield gaps and biomass gaps ranged between 8.8Mgha−1 to 10.0Mgha−1 and 14.8Mgha−1 to 17.1Mgha−1 respectively across the districts. Thus average farmer’s yield and biomass is only 17% and 13% of the simulated water-limited yield and biomass respectively. The spatial and temporal variability in yield gap was positively correlated with the radiation during the crop growing period. Associated spatial variability in biomass gap was positively correlated with radiation and negatively with the precipitation, whereas temporal variability in the biomass gap was positively correlated with the radiation during the crop growing period. Thus, under the current input intensities in humid, tropical Central Ghana, neither maize grain and biomass yields nor the potential water limited yields are significantly positively related to precipitation during the growing cycle. Closing the large yield gaps will require in the first place adequate supply of nutrients.

Can Bangladesh produce enough cereals to meet future demand?

Bangladesh faces huge challenges in achieving food security due to its high population, diet changes, and limited room for expanding cropland and cropping intensity. The objective of this study is to assess the degree to which Bangladesh can be self-sufficient in terms of domestic maize, rice and wheat production by the years 2030 and 2050 by closing the existing gap (Yg) between yield potential (Yp) and actual farm yield (Ya), accounting for possible changes in cropland area. Yield potential and yield gaps were calculated for the three crops using well-validated crop models and site-specific weather, management and soil data, and upscaled to the whole country. We assessed potential grain production in the years 2030 and 2050 for six land use change scenarios (general decrease in arable land; declining ground water tables in the north; cropping of fallow areas in the south; effect of sea level rise; increased cropping intensity; and larger share of cash crops) and three levels of Yg closure (1: no yield increase; 2: Yg closure at a level equivalent to 50% (50% Yg closure); 3: Yg closure to a level of 85% of Yp (irrigated crops) and 80% of water-limited yield potential or Yw (rainfed crops) (full Yg closure)). In addition, changes in demand with low and high population growth rates, and substitution of rice by maize in future diets were also examined. Total aggregated demand of the three cereals (in milled rice equivalents) in 2030 and 2050, based on the UN median population variant, is projected to be 21 and 24% higher than in 2010. Current Yg represent 50% (irrigated rice), 48–63% (rainfed rice), 49% (irrigated wheat), 40% (rainfed wheat), 46% (irrigated maize), and 44% (rainfed maize) of their Yp or Yw. With 50% Yg closure and for various land use changes, self-sufficiency ratio will be >1 for rice in 2030 and about one in 2050 but well below one for maize and wheat in both 2030 and 2050. With full Yg closure, self-sufficiency ratios will be well above one for rice and all three cereals jointly but below one for maize and wheat for all scenarios, except for the scenario with drastic decrease in boro rice area to allow for area expansion for cash crops. Full Yg closure of all cereals is needed to compensate for area decreases and demand increases, and then even some maize and large amounts of wheat imports will be required to satisfy demand in future. The results of this analysis have important implications for Bangladesh and other countries with high population growth rate, shrinking arable land due to rapid urbanization, and highly vulnerable to climate change.

Establishment and application of spring maize yield to evapotranspiration boundary function in the Loess Plateau of China

The yield to evapotranspiration (Y/ET) boundary function is a simple method for estimating water-limited yield potential and provides a guide for making proper crop management decisions in dryland areas. However, the original approach is arbitrary, and the boundary function is established without following any specific principles. In the present study, based on French & Schultz’s boundary function concept and the quantile regression theory, and also linear interpolation to eliminate outliers, we developed an improved method in establishing the Y/ET boundary function. Taking spring maize in the Loess Plateau of China as a case study we selected fundamental data points obtained from 887 records found in 65 papers that were published between 1987 and 2014, and also linear interpolation to eliminate outliers. The results showed that the Y/ET boundary function was y=60.5×(x−50) with a plateau yield of 15,954kgha−1 when ET exceeded 314mm. Without film mulching, the Y/ET boundary function was y=47.5×(x−62.3) with a plateau yield of 12,840kgha−1 when ET exceeded 325mm. We further compared three different crop production methods (plastic film mulching, straw mulching, and no mulching) on maize production in the Loess Plateau. Among these methods, plastic film mulching was the most effective, since it increased the yield potential of spring maize, regardless ET, and hence, improved water use efficiency. Overall, this study provides novel insights into the establishment of the Y/ET boundary functions using specific principles that increase accuracy and help to identify the most profitable crop management systems.

Temporal Variations of Water Productivity in Irrigated Corn: An Analysis of Factors Influencing Yield and Water Use across Central Nebraska.

Water Productivity (WP) of a crop defines the relationship between the economic or physical yield of the crop and its water use. With this concept it is possible to identify disproportionate water use or water-limited yield gaps and thereby support improvements in agricultural water management. However, too often important qualitative and quantitative environmental factors are not part of a WP analysis and therefore neglect the aspect of maintaining a sustainable agricultural system. In this study, we examine both the physical and economic WP in perspective with temporally changing environmental conditions. The physical WP analysis was performed by comparing simulated maximum attainable corn yields per unit of water using the crop model Hybrid-Maize with observed data from 2005 through 2013 from 108 farm plots in the Central Platte and the Tri Basin Natural Resource Districts of Nebraska. In order to expand the WP analysis on external factors influencing yields, a second model, Maize-N, was used to estimate optimal nitrogen (N)–fertilizer rate for specific fields in the study area. Finally, a vadose zone flow and transport model, HYDRUS-1D for simulating vertical nutrient transport in the soil, was used to estimate locations of nitrogen pulses in the soil profile. The comparison of simulated and observed data revealed that WP was not on an optimal level, mainly due to large amounts of irrigation used in the study area. The further analysis illustrated year-to-year variations of WP during the nine consecutive years, as well as the need to improve fertilizer management to favor WP and environmental quality. In addition, we addressed the negative influence of groundwater depletion on the economic WP through increasing pumping costs. In summary, this study demonstrated that involving temporal variations of WP as well as associated environmental and economic issues can represent a bigger picture of WP that can help to create incentives to sustainably improve agricultural production.

Yield gap analysis of rainfed wheat demonstrates local to global relevance

SUMMARYAustralia 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 (R2 = 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 (R2 = 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 (R2 = 0·66), and a positive relationship between Y% and Yw variability (R2 = 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.

Data rich yield gap analysis of wheat in Australia

Closing the gap between yields currently achieved on farms and those that can be achieved with best practice and current technology in a given environment is a key strategy for increasing grain production on existing cropland. In this study, the yield gap of rainfed wheat in Australia was derived by a data rich (DR) analysis method made possible by the high density of data available in the Australian grain zone. At the national scale, the analysis revealed that Australia’s 15year (1996–2010) average actual wheat yield (Ya) was 1.7t/ha, while the simulated water-limited yield potential (Yw) was 3.5t/ha; thus the yield gap (Yg=Yw-Ya) was 1.8t/ha; and the relative yield (Y%=100x Ya/Yw) was 49%. The fifteen year average Ya and Yw values for each of the 245 statistical local areas (SLAs) in Australia’s wheat zone were strongly and positively correlated (Ya=0.456x Yw+0.15; R2=0.65; RMSE=0.291; p<0.001) such that the yield gap (Yw-Ya) tended to be larger in SLAs with higher Yw values. Mean Y% values vary among SLAs from a low of 34% to a high of 69% (standard deviation=7.7%). The extremely tight correlation, at the national scale (Ya=0.488x Yw+0.021; R2=0.95; RMSE=0.087; p<0.001), between the methodologically independent estimates of Yw and Ya values points to the consistency of both estimates. It also indicates the potential for using updated Yw estimates to help derive more reliable national crop yield forecasts in real time. The results of this analysis were compared with those of two alternative analyses of Australia’s wheat yield gaps using (1) EarthStat, a statistically based global yield gap analysis methodology and (2) the Global Yield Gap Atlas (GYGA) designed to achieve locally credible assessments of yield gaps for developing regions such as sub-Saharan Africa (SSA) and South Asia (SA) with the limited data typically available in such countries. At the national level all three methods gave similar results. However, at the sub-national level (based on agroecological zones) differences emerged between the water-limited yield potential values of EarthStat on the one hand and those of the DR and GYGA methods on the other. Given the high spatial environmental variability within the Australian wheat zone the remarkable level of agreement between results from these two methodologies provides evidence of the robustness of the GYGA protocols. The advantage of the DR method is in its higher resolution and the subsequent local relevance of results.

Impact of Spatial Soil and Climate Input Data Aggregation on Regional Yield Simulations.

We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.

Elevated atmospheric [CO2] can dramatically increase wheat yields in semi‐arid environments and buffer against heat waves

Wheat production will be impacted by increasing concentration of atmospheric CO2 [CO2 ], which is expected to rise from about 400μmolmol(-1) in 2015 to 550μmolmol(-1) by 2050. Changes to plant physiology and crop responses from elevated [CO2 ] (e[CO2 ]) are well documented for some environments, but field-level responses in dryland Mediterranean environments with terminal drought and heat waves are scarce. The Australian Grains Free Air CO2 Enrichment facility was established to compare wheat (Triticum aestivum) growth and yield under ambient (~370μmol(-1) in 2007) and e[CO2 ] (550μmol(-1) ) in semi-arid environments. Experiments were undertaken at two dryland sites (Horsham and Walpeup) across three years with two cultivars, two sowing times and two irrigation treatments. Mean yield stimulation due to e[CO2 ] was 24% at Horsham and 53% at Walpeup, with some treatment responses greater than 70%, depending on environment. Under supplemental irrigation, e[CO2 ] stimulated yields at Horsham by 37% compared to 13% under rainfed conditions, showing that water limited growth and yield response to e[CO2 ]. Heat wave effects were ameliorated under e[CO2 ] as shown by reductions of 31% and 54% in screenings and 10% and 12% larger kernels (Horsham and Walpeup). Greatest yield stimulations occurred in the e[CO2 ] late sowing and heat stressed treatments, when supplied with more water. There were no clear differences in cultivar response due to e[CO2 ]. Multiple regression showed that yield response to e[CO2 ] depended on temperatures and water availability before and after anthesis. Thus, timing of temperature and water and the crop's ability to translocate carbohydrates to the grain postanthesis were all important in determining the e[CO2 ] response. The large responses to e[CO2 ] under dryland conditions have not been previously reported and underscore the need for field level research to provide mechanistic understanding for adapting crops to a changing climate.

Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops

We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known.

Exploring the challenges with soil data in regional land use analysis

Over recent decades, environmental models have gradually replaced traditional, qualitative land evaluation in regional land use analysis (RLUA). This changed the data requirements as the environmental models require quantitative, high resolution and spatially exhaustive data. As resources to collect new data are limited, RLUA often relies on already existing data. These data often do not meet the data requirements for the environmental models. Hence, a gap developed between the supply and demand of data in RLUA. This study aims to explore and analyse the effect of using different soil datasets in a case study for Machakos and Makueni counties (Kenya). Six soil datasets were available for the study area and showed large differences. For example, average clay percentages varied between 11.7% and 44.4%. The soil datasets were developed under different assumptions on e.g., soil variability. Four assumptions were verified using a field survey. An ongoing RLUA, the Global Yield Gap Atlas (GYGA) project, was taken as a case study to analyse the effect of using different soil datasets. The GYGA project aims to assess yield gaps defined as the difference between potential or water-limited yields and actual yields. Rain-fed maize is the dominating cropping system in Machakos and Makueni counties. The GYGA project uses soil data for the selection of the most dominant maize growing areas and to simulate water-limited maize yields. The protocols developed by the GYGA project were applied to the six soil datasets. This resulted in the selection of six different maize-growing areas and different water-limited maize yields. Our study clearly demonstrates the large differences between soil datasets. Main challenges with soil data in RLUA are: i) understand the assumptions in soil datasets, ii) create soil datasets that meet the requirements for regional land use analysis, iii) not only rely on legacy soil data but also collect new soil data and iv) validate soil datasets.

Nitrogen management to optimise canola production in Australia

The expansion of canola production in Australia coincided with an increase in cropping intensity and a reduction in pastures and tillage. These changes mean that nitrogen (N) is often recognised as the most limiting nutrient in canola production, and is the largest single input cost for many growers. Canola responds to added N by producing larger plants that results in a longer leaf area duration, building a larger photosynthetic canopy for seed filling. Although the crop can compensate for poor early growth, a larger canopy is able to compete more effectively against weeds and helps reserve water for crop transpiration rather than soil evaporation. Nitrogen uptake is most rapid during stem elongation, and the N acquired can be remobilised to developing pods and then to seeds. Unlike wheat, N uptake can continue until drought or high temperatures prevent further assimilate supply to the reproductive apex. Data from Australian experiments that measured N uptake over the whole growth period showed that each tonne of seed required ~80 kg N to be taken up, and this forms the basis of a budgeting approach for determining N supply. Typically, added N reduces seed oil concentration at a rate of between –0.03 and –0.13%/kg N. Despite this decline due to added N, oil yield usually increases and the overall value of the crop also increases. Nitrogen has little impact on oil quality or seed glucosinate concentration. The efficiency and effectiveness of N management depends first on selecting a rate appropriate to the water-limited yield potential. Most growers estimate the N rate required using an N budget based on supplying 80 kg N/t less indigenous N supply. The budgeted N can be split over two, three or even more applications with little loss in agronomic efficiency. Splitting application enables growers to make decisions about N when there is more certainty about seasonal conditions. Urea is the most common N source used, and unless there are particular loss processes that are likely to occur, it is cheap and effective. Suggested areas for future N research on canola are to develop tools that can assess in-crop N status, an evaluation of late season N product rate and timing particularly on seed oil concentration, N management for grazed canola, and the development of guidelines to identify, and then address, particular N loss pathways using enhanced efficiency fertilisers.

Analysis of chickpea yield gap and water-limited potential yield in Iran

Iran is one of the major chickpea (Cicer arietinum L.) producing countries of the world. Average chickpea yield in Iran is about 500kg ha−1 while the world average is 900kgha−1. The objective of this study was to investigate chickpea water limited potential yield (Yw) and yield gap in Iran. The analysis was based on data from five representing chickpea producing locations of Iran. Estimated country Yw and yield gap were 991 and 463kgha−1, respectively, indicating that farmers have reached 53% (range: 38–64%) of Yw. If farmers could reach 80% of Yw of their locations, by improving agronomy practice, country average yield would increase by 50%, from 528 to 793kgha−1. A key finding of the study was that chickpea yield in Iran is largely limited by inefficient use of environmental resources and not the genetics of the current cultivars. Much higher yield increase is obtainable via an agronomic option than a genetic improvement option: using shorter duration cultivars increased Yw to 1237kgha−1 (25% increase), but applying a single irrigation of 60mm at first-pod alone or in combination with shorter duration cultivars increased Yw to 1804kgha−1(85% increase) and to 1997kgha−1 (104% increase), respectively. Thus, tripling chickpea production would be feasible using a single irrigation with or without shorter duration cultivars (from 528 to 1443 or 1598kgha−1). The availability of water for the single irrigation is discussed.