Water-limited Potential Yield Research Articles

Definitions and determination of crop yield, yield gaps, and of rates of change

Given the importance of crop yield and yield progress, this review endeavours to clearly define the different representations of yield, discuss their measurement, and elucidate some controlling factors in yield change. For a field, farm, district or region, average farm or actual yield (FY) is central, but potential (and water-limited potential) yield (PY, PYw) is also an important yardstick. PY is defined here as the measured yield of the best cultivar, grown with optimal agronomy and without manageable biotic and abiotic stresses, under natural resource and cropping system conditions representative of the target area. Economic yield, governed by considerations of profit and risk, and record and theoretical yield, complete the picture. Yield gap is defined as the difference between PY and FY under the same environment. Across most crop-region combinations in the last 2 to 3 decades, FY progress has been associated with both PY progress and yield gap closing, and a simple model, based on linear regression against time, is proposed for understanding this. PY advance is the result of plant breeding and new agronomy (and their interaction, usually positive), while yield gap closing arises with the adoption by farmers of known innovations faster than new ones are invented. Unravelling the true technological component in apparent progress in PY, and especially in FY, is not necessarily simple, and confounding factors are listed and discussed.

Simulating potential growth and yield of oil palm (Elaeis guineensis) with PALMSIM: Model description, evaluation and application

Reducing the gap between water-limited potential yield and actual yield in oil palm production systems through intensification is seen as an important option for sustainably increasing palm oil production. Simulation models can play an important role in quantifying water-limited potential yield, and therefore the scope for intensification, but no oil palm model exists that is both simple enough and at the same time incorporates sufficient plant physiological knowledge to be generally applicable across sites with different growing conditions. The objectives of this studytherefore were to develop a model (PALMSIM) that simulates, on a monthly time step, the potential growth of oil palm as determined by solar radiation and to evaluate model performance against measured oil palm yields under optimal water and nutrient management for a range of sites across Indonesia and Malaysia. The maximum observed yield in the field matches the corresponding simulated yield for dry bunch weight with a RMSE of 1.7Mgha−1year−1 against an observed yield of 18.8Mgha−1. Sensitivity analysis showed that PALMSIM is robust: simulated changes in yield caused by modifying the parameters by 10% are comparable to other tree crop model evaluations. While we acknowledge that, depending on the soils and climatic environment, yields may be often water limited, we suggest a relatively simple physiological approach to simulate potential yield, which can be usefully applied to high rainfall environments and is considered as a first step in developing an oil palm model that also simulates water-limited potential yield. To illustrate the application possibilities of the model, PALMSIM was used to create a potential yield map for Indonesia and Malaysia by simulating the growth and yield at a resolution of 0.1°. This map of potential yield is considered as a first step towards a decision support tool that can identify potentially productive, but at the moment degraded sites in Indonesia and Malaysia.

YIELD GAP ANALYSIS OF CHICKPEA UNDER SEMI-ARID CONDITIONS: A SIMULATION STUDY

Yield gap analysis provides an essential framework to prioritize research and policy efforts to reduce yield constraints. To identify options for increasing chickpea yield, the SSM-chickpea model was parameterized and evaluated. The model was applied to analyze seed yield at both potential and water limited production levels and subsequently to find the yield gap for nine different locations. Study locations are selected to represent major chickpea-growing areas of Khorasan Razavi province (located between 37 ◦ N and 33 ◦ S latitude and 61 ◦ E and 56 ◦ W longitude). The average simulated potential yield of chickpea across all study locations was 2251 kg ha -1 , while for the water limited yield was 1026 kg ha -1 , indicating 54% lower due to adverse soil moisture conditions. Average irrigated and rainfed actual yields were 64% and 79% less than simulated potential and water limited yields respectively. Maximum and minimum of simulated potential yield minus simulated water limited yield (YGMM) and simulated potential yield minus irrigated actual yield (YGMI) were observed in Torbat-Jam and Quchan, respectively. Generally, YGMI and YGMM showed an increasing trend from the north (including Neishabur, Mashhad, Quchan and Daregaz) to the south of the study province (Torbat-Jam and Gonabad). In comparison to other indices, simulated water limited yield minus rainfed actual yield (YGMR) was very low because both simulated water limited and average rainfed actual yields were low in these locations. Overall, YGMR was almost unaffected by the quantity of rainfall received at these locations.

Leading farmers in South East Australia have closed the exploitable wheat yield gap: Prospects for further improvement

Australian dry-land crop producers farm in regions with highly variable climate and soils. Farmers have responded to the pressures of rising costs by pioneering and adapting new technologies to narrow the gap between actual and water-limited yield. With yields reaching a plateau in many of the developed world’s cropping areas, it is possible that Australia’s leading farmers have similarly closed the exploitable yield gap and require technological breakthroughs to sustainably push the production frontier to new and higher levels. To assess the potential for Australian farmers to continue closing the yield gap, and possibly increase water-limited yield, the long-term farm production records of individual wheat fields of three leading farmers in South East Australia were used to ascertain the applicability of modelling to develop new and innovative practices. The cropping systems simulator APSIM was used to establish the attainable simulated yield based on the farmers’ chosen management inputs for wheat crops over a period of 16–20 years. A strong relationship (r2=0.89, RMSD=508kgha−1) was found between actual and simulated yields. This relationship indicates that yield-reducing factors not simulated by APSIM (weeds, disease etc.) were largely controlled on these farms and confirms APSIM’s suitability for this analysis. Over the 16–20 year study period, the average yield gaps on the three farms ranged from 480 to 770kgha−1; representing between 74 and 82% of their water-limited yield potential. For these leading farmers, the yield gap is only small and unlikely to be economically exploitable under current management practices. Consequently, three tactical management innovations with potential to improve farm wheat yield and reduce risk were evaluated. One innovation investigated whether farmers practicing no-till crop establishment, who were able to control weeds prior to sowing, could benefit from sowing current cultivars earlier than present-day practice. It was found that leading farmers are already sowing at the optimum time and sowing earlier would not increase yield because of greater risk of frost damage. Two other innovations were found to have practical application. The first used Yield Prophet® to assist farmers decide when to apply in-crop nitrogen fertiliser based on a more complete understanding of nitrogen and water requirements of crops in variable growing seasons. The second innovation involved sowing slow maturing wheat cultivars earlier than current practice but only in years with adequate stored soil water and early season rainfall. Both innovations were found to increase grain yield and reduce risk of over- or under-application of nitrogen fertiliser. Investigation of strategic and tactical management options to increase yield using simulation modelling for subsequent evaluation in the field has the potential to keep Australian farmers at the forefront of innovations in crop production.

Impact of derived global weather data on simulated crop yields

Crop simulation models can be used to estimate impact of current and future climates on crop yields and food security, but require long-term historical daily weather data to obtain robust simulations. In many regions where crops are grown, daily weather data are not available. Alternatively, gridded weather databases (GWD) with complete terrestrial coverage are available, typically derived from: (i) global circulation computer models; (ii) interpolated weather station data; or (iii) remotely sensed surface data from satellites. The present study's objective is to evaluate capacity of GWDs to simulate crop yield potential (Yp) or water-limited yield potential (Yw), which can serve as benchmarks to assess impact of climate change scenarios on crop productivity and land use change. Three GWDs (CRU, NCEP/DOE, and NASA POWER data) were evaluated for their ability to simulate Yp and Yw of rice in China, USA maize, and wheat in Germany. Simulations of Yp and Yw based on recorded daily data from well-maintained weather stations were taken as the control weather data (CWD). Agreement between simulations of Yp or Yw based on CWD and those based on GWD was poor with the latter having strong bias and large root mean square errors (RMSEs) that were 26–72% of absolute mean yield across locations and years. In contrast, simulated Yp or Yw using observed daily weather data from stations in the NOAA database combined with solar radiation from the NASA-POWER database were in much better agreement with Yp and Yw simulated with CWD (i.e. little bias and an RMSE of 12–19% of the absolute mean). We conclude that results from studies that rely on GWD to simulate agricultural productivity in current and future climates are highly uncertain. An alternative approach would impose a climate scenario on location-specific observed daily weather databases combined with an appropriate upscaling method.

Conservation agriculture in African mixed crop-livestock systems: Expanding the niche

Competition for crop residues between livestock feeding and soil mulching is a major cause of the low and slow adoption of conservation agriculture (CA) in sub-Saharan Africa. Retaining crop residues in the field is not only a prerequisite for CA but may also be the most viable option for African farmers to retain their fields in a productive state. In this paper, (1) we explore the possibility of increasing the quantity of crop residue available by closing the maize yield, (2) we propose interventions that can reduce crop residue demand for livestock feed, and (3) we quantify the optimum amount of crop residues required as mulch, using empirical, secondary and modeling data from Western Kenya and the Ethiopian Rift Valley. Residue retention can also be increased by reducing livestock demand. Closing the maize yield gap–i.e. achieve 90% of the water-limited yield potential–and intensifying dairy production–which would promote the use of rations that are more energy-dense than cereal residue-based rations–would increase the estimated proportion of farmers retaining at least 1tha−1 of crop residues from the current 36% to 97% in Western Kenya. In the Ethiopian Rift Valley, closing the maize yield gap and substituting mechanization to animal draught power would increase the estimated proportion of farmers retaining at least 1tha−1 of crop residues from the current 3% to 83%. We conclude that the question is not ‘if’, but ‘how’ cereal residues can fulfill the demand of both the soil and the livestock.

Quantifying the spatial pattern of the yield gap within a farm in a low rainfall Mediterranean climate

Quantification of the gap between water-limited yield and actual crop yields at a sub-field scale across grain farms are now becoming possible in industrialised agriculture because of routine yield mapping and readily available methods to estimate potential yield. Analyses at sub-field scale can be used to target intervention strategies such as matching fertiliser inputs to yield variation or amelioration of soil constraints. As such they complement the predominant “top-down” approaches to the quantification of yield gaps and are relevant to individual farm decision making. In this study we bring together yield maps, farm soil surveys, and rainfall records to generate whole-farm maps of actual yield, water-limited potential yield, the yield gap, the contribution of soil constraints to the yield gap, and a list of the feasible interventions to close that gap. We apply the approach in a case study of a 4500ha rainfed farm, located in the low-rainfall cropping zone of the Western Australian grainbelt. We found a substantial gap between water-limited potential yield and actual yield, in an environment where wheat yields typically range between 0.5 and 2.5t/ha. On a whole-of-farm basis, mean wheat yield was 50–60% of the water-limited yield and the yield gap varied from 0.6t/ha in a dry year to 1.5t/ha in wet year. The size of the yield gap varied among and within soil types due to associated soil constraints, and the analysis suggested that addressing the predominant constraint of acidity on the average performing areas on the farm could generate the greatest gains, rather than focussing on the below-average areas that have large yield gaps, which are afflicted by intractable soil constraints.The approach can be used by advisors and farmers to assess the proportion of the farm that reaches attainable or water-limited yield, and a tool to target regions within the farm that fail to reach attainable yield. This style of analysis lends itself to landscapes where there is a strong association between crop yield and soil type and environments where yield is strongly determined by rainfall and hence water-limited yield can be easily calculated. With an increase in the routine use of yield monitors by grain growers we anticipate an increase in yield map coverage across farms that will eventually make our method readily applicable.

Use of agro-climatic zones to upscale simulated crop yield potential

Yield gap analysis, which evaluates magnitude and variability of difference between crop yield potential (Yp) or water limited yield potential (Yw) and actual farm yields, provides a measure of untapped food production capacity. Reliable location-specific estimates of yield gaps, either derived from research plots or simulation models, are available only for a limited number of locations and crops due to cost and time required for field studies or for obtaining data on long-term weather, crop rotations and management practices, and soil properties. Given these constraints, we compare global agro-climatic zonation schemes for suitability to up-scale location-specific estimates of Yp and Yw, which are the basis for estimating yield gaps at regional, national, and global scales. Six global climate zonation schemes were evaluated for climatic homogeneity within delineated climate zones (CZs) and coverage of crop area. An efficient CZ scheme should strike an effective balance between zone size and number of zones required to cover a large portion of harvested area of major food crops. Climate heterogeneity was very large in CZ schemes with less than 100 zones. Of the other four schemes, the Global Yield Gap Atlas Extrapolation Domain (GYGA-ED) approach, based on a matrix of three categorical variables (growing degree days, aridity index, temperature seasonality) to delineate CZs for harvested area of all major food crops, achieved reasonable balance between number of CZs to cover 80% of global crop area and climate homogeneity within zones. While CZ schemes derived from two climate-related categorical variables require a similar number of zones to cover 80% of crop area, within-zone heterogeneity is substantially greater than for the GYGA-ED for most weather variables that are sensitive drivers of crop production. Some CZ schemes are crop-specific, which limits utility for up-scaling location-specific evaluation of yield gaps in regions with crop rotations rather than single crop species.

Geospatial Approach in Assessing Agro-Climatic Suitability of Soybean in Rainfed Agro-Ecosystem

The present study demonstrated the method- ology to assess agro-climatic suitability of the soybean crop through integration of crop suitability based on FAO framework of land evaluation and biophysical (wa- ter limited) yield potential in the rainfed agro-ecosystem. A long term climatic database (1980-2003) was pre- pared to compute decadal rainfall and temperature var- iations of 13 IMD stations in part of Madhya Pradesh state. The climatic database was used in soil water bal- ance software-BUDGET to compute crop specific length of growing period (LGP) and biophysical produc- tion potential such as water limited crop yield potential of each soil types for soybean crop. Water limited crop yieldpotentialofsoilswerefoundtobevariedfrom33to 100 and LGP ranged from 65 to 180 days in the area. FAO based land suitability was analyzed in association with the water limited yield potential for better appraisal of land potential and assess their suitability in rainfed area. FAO basedland suitability indicated2.45% area as highly suitable and 57.49 % area as moderately suitable. However, integration of water limited crop yield poten- tial with FAO based land suitability lead to agro-climatic suitability analysis indicated 17.60 % and 40.03 % area, respectively as highly suitable and moderately suitable. FAO based land evaluation showed 88.13 % of plains as moderately suitable whereas agro-climatic suitability in- dicated only 47.79 %. Agro-climatic suitability analysis revealed undulating plateau and undulating plains as most suitable for soybean crop.

When yield gaps are poverty traps: The paradigm of ecological intensification in African smallholder agriculture

Yield gaps are pervasive in African smallholder agriculture, and are large for almost all crops in all regions. There is consensus that poor soil fertility and nutrient availability are the major biophysical limitations to agricultural production in the continent. We identify two major yield gaps: (1) the gap between actual yields (YA) and the water-limited yield potential (Yw), which is the maximum yield achievable under rainfed conditions without irrigation if soil water capture and storage is optimal and nutrient constraints are released, and (2) The gap between YA, and a locally attainable yield (YL) which corresponds to the water and nutrient-limited yields that can be measured in the most productive fields of resource endowed farmers in a community. Estimates of these two yield gaps are given for major crops, together with a framework for how yield gaps can be estimated in a pragmatic way for different farming systems. The paradigm of ecological intensification which focuses on yield potential, soil quality and precision agriculture is explored for the African context. Our analysis suggests that smallholder farmers are unable to benefit from the current yield gains offered by plant genetic improvement. In particular, continued cropping without sufficient inputs of nutrients and organic matter leads to localised but extensive soil degradation and renders many soils in a non-responsive state. The lack of immediate response to increased inputs of fertiliser and labour in such soils constitutes a chronic poverty trap for many smallholder farmers in Africa. This necessitates a rethink for development policy aimed to improve productivity and address problems of food insecurity.

Risk assessment of agricultural drought using the CERES-Wheat model: a case study of Henan Plain, China

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 50:247-256 (2011) - DOI: https://doi.org/10.3354/cr01060 Contribution to the CR Special: 'Changes in climatic extremes over mainland China' Risk assessment of agricultural drought using the CERES-Wheat model: a case study of Henan Plain, China Haiyan Zhao1, Ge Gao2,*, Xiaodong Yan3,4, Qiang Zhang5, Meiting Hou6,Yeyu Zhu7, Zhan Tian8 1Shanxi Climate Center, 80 Pingyang Street, 030006 Taiyuan, PR China 2National Climate Center, China Meteorological Administration, 46 Zhongguancun South Street, 100081 Beijing, PR China 3Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, 40 Huayanli, 100029 Beijing, PR China 4Beijing Normal University, 19 Xinjiekouwai Street, 100875 Beijing, PR China 5China Meteorological Administration, 46 Zhongguancun South Street, 100081 Beijing, PR China, 6China Meteorological Administration Training Centre, 46 Zhongguancun South Street, 100081 Beijing, PR China 7Henan Climate Center, 110 Jinshui Road, 450003 Zhengzhou, PR China 8Shanghai Climate Center, 166 Puxi Road, 200030 Shanghai, PR China *Corresponding author. Email: gaoge@cma.gov.cn ABSTRACT: Droughts caused by a lack of precipitation are one of the major factors limiting agricultural crop production. It is thus important to assess the risk of such droughts in order to reduce their effect on agriculture. In the present study, the drought risk for crop production was assessed through an integrated approach that analyzed the relationship between crop yield and drought on the Henan Plain, China. We used the calibrated CERES-Wheat model to simulate 2 levels of wheat yield, the yield potential and the water-limited yield potential, at 66 weather stations. The yield gap between the yield potential and the water-limited yield potential was used as an indicator of the effects of a precipitation deficit on crop production under rain-fed conditions. A strong linear relationship between the yield gap and the amount of precipitation in the growing season was observed for each station during the period 1962−2009. A uniform criterion for drought severity thresholds for the entire Henan Plain was constructed based on the yield gap. For each station, the growing-season precipitation thresholds associated with different drought severities were then calculated based on the linear relationship between the yield gap and the amount of precipitation in the growing season. Drought frequencies derived from changes in the amount of precipitation during the growing season were also examined for all stations and spatially interpolated over the plain. The results showed diverse spatial patterns of frequency with respect to different drought types. Light droughts often occurred in the southern region, and moderate droughts occurred more frequently in the western and eastern regions. Severe drought displayed a generally decreasing trend from north to south. KEY WORDS: Crop model · Yield potential · Water-limited yield potential · Thresholds · Drought frequency Full text in pdf format PreviousCite this article as: Zhao H, Gao G, Yan X, Zhang Q, Hou M, Zhu Y, Tian Z (2011) Risk assessment of agricultural drought using the CERES-Wheat model: a case study of Henan Plain, China. Clim Res 50:247-256. https://doi.org/10.3354/cr01060 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 50, No. 2-3. Online publication date: December 22, 2011 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2011 Inter-Research.

Wheat physiology: a review of recent developments

This review focuses on recent advances in some key areas of wheat physiology, namely phasic development, determination of potential yield and water-limited potential yield, tolerance to some other abiotic stresses (aluminium, salt, heat shock), and simulation modelling. Applications of the new knowledge to breeding and crop agronomy are emphasized. The linking of relatively simple traits like time to flowering, and aluminium and salt tolerance, in each case to a small number of genes, is being greatly facilitated by the development of molecular gene markers, and there is some progress on the functional basis of these links, and likely application in breeding. However with more complex crop features like potential yield, progress at the gene level is negligible, and even that at the level of the physiology of seemingly important component traits (e.g., grain number, grain weight, soil water extraction, sensitivity to water shortage at meiosis) is patchy and generally slow although a few more heritable traits (e.g. carbon isotope discrimination, coleoptile length) are seeing application. This is despite the advent of smart tools for molecular analysis and for phenotyping, and the move to study genetic variation in soundly-constituted populations. Exploring the functional genomics of traits has a poor record of application; while trait validation in breeding appears underinvested. Simulation modeling is helping to unravel G × E interaction for yield, and is beginning to explore genetic variation in traits in this context, but adequate validation is often lacking. Simulation modelling to project agronomic options over time is, however, more successful, and has become an essential tool, probably because less uncertainty surrounds the influence of variable water and climate on the performance of a given cultivar. It is the ever-increasing complexity we are seeing with genetic variation which remains the greatest challenge for modelling, molecular biology, and indeed physiology, as they all seek to progress yield at a rate greater than empirical breeding is achieving.

Growing-season rainfall, ear number and the water-limited potential yield of wheat in south-western Australia

Knowledge of the contribution of ear number per unit area, grains per ear, and kernel weight to grain yield is important in understanding the limits to the water-limited potential yield of wheat in rain-fed agricultural regions. This paper analyses the relationships between grain yield, yield components and growing-season rainfall using data from the low-, medium-, and high-rainfall zones of the cropping region of south-western Australia. The aim of the paper is to quantify the contribution of ear number, grains per ear and kernel weight to grain yield and define the yield components of wheat required to achieve its water-limited yield. The grain yield of wheat was closely related to the number of grains per unit area (r2 = 0.90) and ears/m2 (r2 = 0.75), but poorly correlated with kernel weight (r2 = 0.30) and grains/ear (r2 = 0.09). The number of grains per unit area was highly related (r2 = 0.75) to the number of ears per unit area across the rainfall zones of the cropping region, and strongly correlated (r2 = 0.88–0.94) to the spike dry weight at anthesis. The highest yields achieved in the field were close to the water-limited potential estimated using the French and Schultz method. To achieve this water-limited potential, wheat requires 1 ear per m2 for every mm of growing-season rainfall (r2 = 0.92). We conclude that the number of ears per unit area is the most important factor required in order to achieve high yields of up to a yield of 6 t/ha in wheat in rain-fed south-western Australia.

Integrating farmer knowledge, precision agriculture tools, and crop simulation modelling to evaluate management options for poor-performing patches in cropping fields

Cropping fields often have poor-performing patches. In an attempt to increase production on poor patches, farmers may apply additional fertiliser or ameliorants without economic or scientific justification. Improved understanding of the extent and causes of poor performance, management options, potential crop yield and economic benefits can give farmers the tools to consider management change. This paper presents an approach to integrating farmer knowledge, precision agriculture tools and crop simulation modelling to evaluate management options for poor-performing patches. We surveyed nine cropping fields in Western Australia and showed that (1) farmers have good understanding of the spatial extent and rank performance of poor-performing areas, when compared to NDVI or yield maps, (2) there is a wide range of physical and chemical soil constraints to crop yield in such patches, some of which can be ameliorated to raise yield potential, and others where crop inputs such as fertiliser can be better matched to low yield potential. Management options for poor-performing patches were evaluated through simulation analysis by removal of constraints to rooting to varying extents, and hence plant available water capacity. These examples show that if the constraint is mis-diagnosed then the potential benefits from amelioration can be overstated. In many cases constraints, often associated with physical limitations such as shallow available rooting depth or light-texture cannot be ameliorated or are uneconomic to ameliorate. In such cases the best intervention may be to lower crop inputs to better match the water-limited yield potential of such poor-performing areas. This research integrated farmer knowledge and spatial data to define yield zones in which targeted soil sampling and crop simulation were then used to determine yield potential and particular constraints to that potential. The economic costs and benefits of differential zone management were examined under a range of husbandry scenarios and, importantly, the sensitivity of economic gain to mis-diagnosis or errors in defining the zones was tested. This approach provided farmers with a robust and credible method for making decisions about spatial management of their fields.

Improving estimates of water-limited yield of wheat by accounting for soil type and within-season rainfall

Rainfall is the main driver of potential yield in the dryland cropping environment of Australia. Rainfall-based empirically derived models such as that proposed by French and Schultz (1984) (F&S) can be used to determine an upper limit of water-limited potential yield, but F&S often overestimates actual yield as it does not account for rainfall distribution, runoff, drainage, or stored soil water. More complex models are available to predict potential yield more accurately; however, farmers and advisors favour easy-to-use-methods to estimate potential yield. To derive a simple yet accurate method for estimating potential yield, several adjustments to F&S were evaluated: (1) accounting for stored soil water at sowing, (2) varying the value of the intercept between yield and growing-season rainfall (GSR), (3) varying the water-use efficiency of the crops (WUE) according to soil type, and (4) adjustments to GSR depending on soil plant-available water capacity (PAWC). The water-limited potential yields predicted from these methods were compared with simulations from the daily time-step simulation model APSIM and observed wheat yields from 146 dryland wheat crops, managed to water-limited potential yield, covering the 1996–2006 seasons in the Mediterranean-type growing environments of Australia. The original F&S method overestimated actual yields, particularly at high rainfall (GSR > 220 mm) when PAWC was low, and underestimated yields at low rainfall (GSR < 220 mm). Significant improvements to the F&S were achieved with a few simple adjustments. With the addition of a variable intercept (dependent upon GSR), accounting for stored soil water at the start of the season and placing a cap on GSR that is a function of the soil PAWC, the predictive performance (RMSE 624 kg/ha) was similar to that gained with the daily time-step model APSIM (RMSE 419 kg/ha). The improved method gave more realistic estimates of water-limited potential yield, particularly at low and high rainfall and on soils of low PAWC.

Phosphorus management on extensive organic and low-input farms

A synthesis of the Australian literature reporting soil and plant phosphorus (P) status under organic methods of broadacre farming provides clear evidence that available soil P is lower in organic systems, although there have been no reports of farm P balances that might help to explain the lower P concentrations. There is also evidence, which is largely circumstantial, to suggest that P deficiency significantly reduces productivity of broadacre organic farms, but few experiments prove this conclusively because of other confounding factors. An overview of international literature suggests similar findings for mixed farms. Nine case studies further examined the constraints imposed by P on broadacre organic and low-input farms in Australia. Two farms on fertile soils had negative P balances but maintained productivity without fertilisers by ‘mining available’ P reserves. Five extensive organic farms on inherently less fertile soils had positive P balances because P fertiliser was used. Four of these farmers reported low productivity, which was supported by comparisons of wheat yields with estimated water-limited potential yields. Low productivity appeared to be related to P deficiency despite the use of allowable mineral fertilisers, mostly reactive phosphate rock (RPR), on these farms. The apparent ineffectiveness of RPR is most likely due to the modest rainfall at these farms (380–580 mm/year). The highest research priority is to develop effective, allowable fertilisers. Until this has been achieved, or ways of using less labile P have been developed, there is a case for derogation in the Certification Standards to allow the use of soluble forms of P fertiliser on soils with low soil solution P and high soil P-sorption. Two low-input farms practicing pasture-cropping had approximately balanced P budgets and from this perspective were the most sustainable of the farms studied.

Analysis of potential yields and yield gaps of rainfed soybean in India using CROPGRO-Soybean model

To assess the scope for enhancing productivity of soybean ( Glycine max L. Merr.), the CROPGRO-Soybean model was calibrated and validated for the diverse soybean-growing environments of central and peninsular India. The validated model was used to estimate potential yields (water non-limiting and water limiting) and yield gaps of soybean for 21 locations representing major soybean regions of India. The average water non-limiting potential yield of soybean for the locations was 3020 kg ha −1, while the water limiting potential was 2170 kg ha −1 indicating a 28% reduction in yield due to adverse soil moisture conditions. As against this, the actual yields of locations averaged 1000 kg ha −1, which was 2020 and 1170 kg ha −1 less than the water non-limiting potential and water limiting potential yields, respectively. Across locations the water non-limiting potential yields were less variable than water limited potential and actual yields, and strongly correlated with solar radiation during the season ( R 2 = 0.83, p ≤ 0.01). Both simulated water limiting potential yield ( R 2 = 0.59, p ≤ 0.01) and actual yield ( R 2 = 0.33, p ≤ 0.05) had significant but positive and curvilinear relationships with crop season rainfall across locations. The gap between water non-limiting and water limiting potential yields was very large at locations with low crop season rainfall and narrowed down at locations with increasing quantity of crop season rainfall. On the other hand, the gap between water limiting potential yield and actual farmers yield was narrow at locations with low crop season rainfall and increased considerably at locations with increasing amounts of rainfall. This yield gap, which reflects the actual yield gap in rainfed environment, is essentially due to non-adoption of improved crop management practices and could be reduced if proper interventions are made. The simulation study suggested that conservation of rainfall and drought resistant varieties in low rainfall regimes; and alleviation of water-logging and use of water-logging tolerant varieties in high rainfall regimes will be the essential components of improved technologies aimed at reducing the yield gaps of soybean. Harvesting of excess rainfall during the season and its subsequent use as supplemental irrigation would further help in increasing crop yields at most locations.

Can increased nutrition raise cereal yields to the rainfall-limited potential in the high rainfall cropping zone of south Western Australia?

Average yield of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) in the high rainfall cropping zone (>750 mm) of south Western Australia from 1996 to 2001 was 2.5 t/ha. This is far below the water-limited potential yield (water losses of 110 mm, transpiration efficiency of 20 kg/ha.mm) of 6–8 t/ha. Nutrition of the cereal crops has been regarded as one constraint to reaching the potential yield, although grain yield increases (responses) under conventional management practices (a series of full cultivation operations) have been inconsistent. Three experiments, with a total of five trial sites conducted over two seasons, were carried out to test the response of wheat and barley to fertiliser applications of nitrogen (N), phosphorus (P), potassium (K), sulfur (S) and trace elements (TE). Various combinations of nutrients were applied. These ranged from no fertiliser (nil), to farmer practice (N at rates at 34–82 kg/ha, P at 3–17 kg/ha, K at 0–50 kg/ha and S at 4–11 kg/ha), to nutrients calculated to supply the needs of a 6–8 t/ha cereal crop (N, P, K, S, TE). The aim was to determine whether the supply of non-limiting levels of crop nutrients could raise yields to the potential yield as determined by seasonal rainfall. In the drier seasons experienced in 2001 and 2002 at Arthur River and Cranbrook, with growing season rainfall (May–November) up to about 350 mm, it was possible to raise grain yields to levels at or above the calculated rainfall-limited potential with increased nutrition (4.2 t/ha for barley and 4.5 t/ha for wheat). However, in the wetter environment of Boyup Brook in 2002, where seasonal rainfall was greater than 500 mm, extra nutrition by itself was not sufficient to reach the water-limited potential, even where the yields were increased from 3.5 to 5.2 t/ha for wheat and from 3.9 to 4.5 t/ha for barley. Further experimentation is required to clarify the factors limiting responses to nutrition when the growing season rainfall is greater than 500 mm and thus allow greater confidence in extrapolating these results in the high rainfall cropping zone of Western Australia. In wheat, the highest profits were obtained from the complete fertiliser strategy (N, P, K, S, TE). However, for barley, the greatest profits were not obtained with the highest grain yields and fertiliser strategies due to decreased grain quality.

Determining the Causes of Spatial and Temporal Variability of Wheat Yields at Sub-field Scale Using a New Method of Upscaling a Crop Model

Wheat yields in the Mediterranean climate of Western and Southern Australia are often limited by water. Our measurements on a 70 ha growers field showed linear relationships between grain yield and the plant available soil water storage capacity (PAWc) of the top 100 cm of the soil profile. PAWc was linearly related to apparent soil electrical conductivity measured by proximal sensing using electromagnetic induction (EM38). The APSIM wheat model also employs PAWc as one of the systems parameters and simulated linear relationships between PAWc and yield. These relationships were used to transform an EM38-derived PAWc map of the field into yield maps for three major season types (dry, medium and wet) and nitrogen (N) fertiliser management scenarios. The results indicated that the main cause of temporal and spatial yield variability within the field was due to interactions of seasonal rainfall, PAWc and N fertiliser applications. Spatial variability was low in low rainfall years when yields across the field were low and the higher soil water storage capacity sites were often underutilised. With adequate N, spatial variability increased with seasonal rainfall as sites with higher PAWc conserved more water in wet seasons to give higher yield response than sites with low PAWc. The higher yield response of high PAWc sites to rainfall gave rise to larger temporal variability compared with sites with low PAWc. Provision of adequate N is required for the water limited yield potential to be expressed and this increased both spatial and temporal variability. Sites with low PAWc performed poorly irrespective of rainfall and N application. PAWc is inherently low on deep coarse sands; these sites should be considered for a change in land use. Elsewhere, strategic management interventions should aim to improve PAWc through sub-soil amelioration and deep root growth to increase the capital asset of the farm. The resulting increase in yields will occur in favourable seasons and with adequate fertiliser provisions. The largest grain yield response to water and N will be obtained on sites with the highest PAWc and it is at those sites that the greatest profits from fertiliser use could be achieved in wet seasons.

Mineral nitrogen supply from pastures to cereals in three northern Victorian environments

An experiment at 3 sites (Birchip, Elmore and Speed) in the northern Victorian cropping belt compared dry matter (DM) production of short-term (2 year) pastures and their contributions to soil mineral nitrogen (N) and subsequent wheat and barley production. The pastures included different varieties of subterranean clover, annual medic and lucerne, and these were compared with ryegrass-dominant pasture, which represented the experimental control. More productive legume pastures generally resulted in greater accumulation of soil mineral N at sowing of the following cereal at both Elmore and Speed; however, at Birchip, soil mineral N remained high under all treatments. At Elmore and Speed, significant (P<0.10) positive relationships were observed between available N at sowing and subsequent wheat and barley production. Cereal grain yield at Birchip was not associated with available N at sowing. The quantities of soil mineral N available at sowing (152 kg/ha) of the cereals were in excess of crop demand at Birchip. At Elmore, the soil mineral N supply (83 kg/ha) was below that required for wheat and barley to reach their water-limited potential yield (20 kg grain/mm of growing season rainfall). However, at Speed, the supply of soil mineral N (63 kg/ha) was sufficient to achieve the water-limited potential grain yield and to produce malting-grade barley, but not sufficient to elevate wheat grain protein concentrations above 11.5%. In environments with low soil N levels, the amount of residual N following short-term pastures increased the availability of N to following cereals. Whether this is sufficient to satisfy subsequent crop demand is largely determined by water availability in the year of cropping. In cases where available N is already high, short-term pasture phases may have little effect on increasing crop production.