•ORYZA v3 and APSIM-Oryza models were improved to account for salinity effects on rice production.•Variability of soil salinity was represented by a simple linear relationship between salt ...concentration and electrical conductivity.•The derived salinity parameters captured response differences between tolerant (BRRI Dhan47) and non-tolerant variety (IR64).•An increase in salinity parameters of 5 % above the value for IR64 would result in a 3 % increase in simulated yield.
Development and testing of reliable tools for simulating rice production in salt-affected areas are presented in this paper. New functions were implemented in existing crop models ORYZA v3 and the cropping systems modelling framework APSIM. Field experiments covering two years, two different sites, and three varieties were used to validate both improved models. We used the salt balance module in the systems model APSIM to simulate the observed daily soil salinity with acceptable accuracy (RMSEn <35%), whereas ORYZA v3 used measured soil salinity at a given interval of days as a model input. Both models presented similarly good accuracy in simulating aboveground biomass, leaf area index, and grain yield for IR64 over a gradient of salinity conditions. The model index of agreement ranged from 0.86 to 0.99. Variability of yield under stressed and non-stressed conditions was simulated with a RMSE, of 191 kg ha−1 and 222 kg ha−1, respectively, for ORYZA v3 and APSIM-Oryza, corresponding to an RMSEn of 14.8% and 17.3%. These values are within the bounds of experimental error, therefore indicating acceptable model performance. The model test simulating genotypic variability of rice crop responses resulted in similar levels of acceptable model performance with RMSEn ranging from 11.3 to 39.9% for observed total above ground biomass for IR64 and panicle biomass for IR29, respectively. With the improved models, more reliable tools are now available for use in risk assessment and evaluation of suitable management options for rice production in salt-affected areas. The approach presented may also be applied in improving other non-rice crop models to integrate a response to soil salinity − particularly in process-based models which capture stage-related stress tolerance variability and resource use efficiency.
•APSIM was evaluated using an Asian dataset covering 12 countries, numerous soils, crops, and practices.•Assessment was from both crop and soil simulation perspectives, including sequence ...effects.•The model performed well in simulating the diversity of cropping systems to which it was applied.•Input parameter estimation challenges, some indicating possible model deficiencies, were noted.•Desirable future APSIM improvements were identified.
Resource shortages, driven by climatic, institutional and social changes in many regions of Asia, combined with growing imperatives to increase food production whilst ensuring environmental sustainability, are driving research into modified agricultural practices. Well-tested cropping systems models that capture interactions between soil water and nutrient dynamics, crop growth, climate and farmer management can assist in the evaluation of such new agricultural practices. One such cropping systems model is the Agricultural Production Systems Simulator (APSIM). We evaluated APSIM’s ability to simulate the performance of cropping systems in Asia from several perspectives: crop phenology, production, water use, soil dynamics (water and organic carbon) and crop CO2 response, as well as its ability to simulate cropping sequences without reset of soil variables. The evaluation was conducted over a diverse range of environments (12 countries, numerous soils), crops and management practices throughout the region. APSIM’s performance was statistically assessed against assembled replicated experimental datasets. Once properly parameterised, the model performed well in simulating the diversity of cropping systems to which it was applied with RMSEs generally less than observed experimental standard deviations (indicating robust model performance), and with particular strength in simulation of multi-crop sequences. Input parameter estimation challenges were encountered, and although ‘work-arounds’ were developed and described, in some cases these actually represent model deficiencies which need to be addressed. Desirable future improvements have been identified to better position APSIM as a useful tool for Asian cropping systems research into the future. These include aspects related to harsh environments (high temperatures, diffuse light conditions, salinity, and submergence), conservation agriculture, greenhouse gas emissions, as well as aspects more specific to Southern Asia and low input systems (such as deficiencies in soil micro-nutrients).
•We evaluated options for increasing Boro rice production at Satkhira, Bangladesh.•Earlier sowing offers higher grain yields with increased cropping area due to better utilisation of fresh river ...water.•Increases in Boro rice production of up to 4x are possible.•Constraints with late-maturing monsoon rice crops and ineffective sluice gate management must be solved.•Potential problems associated with increased salinization of polder soils need to be investigated.
Increasing Boro (irrigated dry season) rice production in the saline coastal zone (CZ) is part of the Bangladesh Government strategy for meeting its Sustainable Development Goals (SDG’s). However salinity and fresh water shortages during the Rabi (dry) season result in large areas of land remaining uncropped and under-utilised in the CZ, with crop yields below potential. We evaluated a range of options for increasing Boro rice production and farmer profit in this region. These included changes to sowing dates in combination with different polder sluice-gate management strategies aimed at increasing irrigation water supply and cropping area. We employed a case-study approach, using a combination of field experimentation, APSIM cropping systems modelling, and economic analysis, focussing on Satkhira District, Khulna Division. We found the most profitable strategies were to establish Boro rice crops in mid-November, around a month earlier than current farmer practice, on larger portions of land irrigated using river water supplied via the polder canal network. This offers significant increases in both farmer profit and regional production (up to 4x). The reasons for the gains are dual – (1) potential rice yields are higher; and (2) early sowing unleashes the potential of extensive fresh-water availability to greatly increase cropping area, because at that time river salinity levels are low and unlimited amounts of suitable irrigation water are available. Under current practices with later sowing dates (around mid-December), these early-season water resources are hardly used. To achieve the advantages of early-sowing, certain system changes are necessary. Firstly, farmers must adopt early-maturing transplanted Aman (T. Aman) rice cultivars in the monsoon season. Secondly, they must synchronise agronomic timings with fellow farmers in polder sluice-gate management zones to allow efficient gate operation and timely drainage of stagnant monsoon waters from fields in October, followed by early-season establishment of Boro rice crops. The applicability of our findings will vary geographically in the CZ, as a function of prevailing dynamics of river salinity, water tables, soils and climate. To understand the economics at a national scale, our analysis should be extended on a regional basis to estimate regional production gains possible, as well as to assess environmental health risks – particularly related to increased salinization of polder soils. Our analysis suggests that substantial investment in further research and achieving the required social and agronomic changes may be warranted.
•Integrated approach using modeling was used to target best management options in rice growing ecosystems in Myanmar.•Spatial variability of rice crop climatic yield potential was quantified for ...Myanmar.•Changes in sowing date and to improved variety provided yield gains up to 53% above the actual yield in stress prone areas.
Rice in Myanmar is grown in diverse environments, including inland dry zone and salt-affected coastal deltas. This study evaluated management options that could improve productivity and reduce risks of rice crop in stress-prone areas of the country. We selected four sites from two regions in the central dry zone (Wundwin) and the Ayeyarwady delta (Labutta, Bogale and Mawlamyinegyun). We used experimental and survey datasets on farmers’ practices and rice yields from 2012 to 2014 to run the ORYZA model to simulate the climatic yield potential (YP; yield without stress) and the attainable yield under rainfed conditions (YW; yield limited by water), saline conditions (YS; yield limited by salinity), and under conditions of current farmers’ practices (YF; yield in farmers’ practices). Simulated yield responses to different management practices showed spatial variability within and among the selected sites. YP ranged from 5.4 to 11.1 t ha−1, YW ranged from 0.5 to 7.5 t ha−1, and YF ranged from 2.2 to 4.2 t ha−1. In salt-affected areas, average YS ranged from less than 0.1 t ha-1 to 5.6 t ha−1. Yield gains with the choice of an improved variety and adjusted sowing date were estimated at up to 53% above YF. Changing the time of sowing and using improved rice varieties provided the greatest yield gains in salt-affected and drought-prone areas where YF was the least. In areas where YF was greater, the improvement of nitrogen management provided larger benefits than in areas with lower YF. We conclude that an integrated approach using remote-sensing technologies, crop modeling, and a geographic information system is valuable for targeting the best management options to close the yield gap in unfavorable rice environments in Asia.
Extreme weather (high rainfall and temperatures) and challenging soils are sources of uncertainties in the use of current crop models that have been developed for more favorable environments. This ...may limit their applicability to guide and support decision making for the development of new agricultural regions in tropical environments. We evaluated the accuracy of the Agricultural Production Systems Simulator (APSIM) framework in representing yield and development of a range of crops across multiple locations in the Northern Territory of Australia, a tropical region with large potential for agricultural development. Observations of yield, biomass, and phenology for a range of crops from 28 experiments undertaken at three locations were compiled and used to develop simulations undertaken using APSIM version 7.10. Model performance varied with coefficients of determination and concordance correlation coefficients ranging from 0.36 to 0.98 and 0.37 to 0.93, respectively. Instances where model performance was less than ideal were associated with conditions presenting a limited number of observed values. Deviations by the model from yield observations were larger for situations with high‐yielding crops and low daily maximum temperatures during vegetative growth stages. Deviations in phenology were larger for conditions associated with water and N stress. APSIM was capable of representing the yield, biomass, and development of cereal and pulse crops and can be used with confidence to assist the expansion of agriculture in tropical environments such as the Northern Territory of Australia.
Core Ideas
The accuracy of crop models for environments frequented by challenging weather and soils is rarely reported.
We showed that the Agricultural Production Systems Simulator (APSIM) performs well in one such environment, the Northern Territory of Australia.
APSIM can be used with confidence in environments with challenging weather and soil conditions.
•Rice crop responses to soil salinity can be fitted with a logistic function with three parameters.•Tolerant genotype, BRRI Dhan 47 presented 50% of reduction in leaf net photosynthesis and ...transpiration at soil salinity higher than 14dSm−1.•Growth of tolerant genotype as BRRI Dhan 47 was significantly reduced at soil salinity higher than 5dSm−1.
Rice is the staple food for almost half of the world population. In South and South East Asia, about 40% of rice production is from deltaic regions that are vulnerable to salt stress. A quantitative approach was developed for characterizing genotypic variability in biomass production, leaf transpiration rate and leaf net photosynthesis responses to salinity during the vegetative stage, with the aim of developing efficient screening protocols to accelerate breeding varieties adapted to salt-affected areas. Three varieties were evaluated in pots under greenhouse conditions and in the field, with average soil salinity ranging from 2 to 12dSm−1. Plant biomass, net photosynthesis rate, leaf transpiration rate and leaf conductance were measured at regular intervals. Crop responses were fitted using a logistic function with three parameters: 1) maximum rate under control conditions (Ymax), 2) salinity level for 50% of reduction (b), and 3) rate of reduction (a). Variation in the three parameters correlated significantly with variation in plant biomass production under increasing salinity. Salt stress levels that caused 50% reduction in net leaf photosynthesis and transpiration rates were higher in the tolerant genotype BRRI Dhan47 (16.5dSm−1 and 14.3dSm−1, respectively) than the sensitive genotype IR29 (11.1dSm−1 and 6.8dSm−1). In BRRI Dhan47, the threshold beyond which growth was significantly reduced was above 5dSm−1 and the rate of growth reduction beyond this threshold was as low as 4% per unit increase in salinity. This quantitative approach to screening for salinity tolerance in rice offers a means to better understand rice growth under salt stress and, using simulation modelling, can provide an improved tool for varietal characterization.
Greater nitrogen efficiency would substantially reduce the economic, energy and environmental costs of rice production. We hypothesized that synergistic balancing of the costs and benefits for soil ...exploration among root architectural phenes is beneficial under suboptimal nitrogen availability. An enhanced implementation of the functional–structural model OpenSimRoot for rice integrated with the ORYZA_v3 crop model was used to evaluate the utility of combinations of root architectural phenes, namely nodal root angle, the proportion of smaller diameter nodal roots, nodal root number; and L‐type and S‐type lateral branching densities, for plant growth under low nitrogen. Multiple integrated root phenotypes were identified with greater shoot biomass under low nitrogen than the reference cultivar IR64. The superiority of these phenotypes was due to synergism among root phenes rather than the expected additive effects of phene states. Representative optimal phenotypes were predicted to have up to 80% greater grain yield with low N supply in the rainfed dry direct‐seeded agroecosystem over future weather conditions, compared to IR64. These phenotypes merit consideration as root ideotypes for breeding rice cultivars with improved yield under rainfed dry direct‐seeded conditions with limited nitrogen availability. The importance of phene synergism for the performance of integrated phenotypes has implications for crop breeding.
Summary Statement
Multiscale mechanistic modelling identified several integrated root phenotypes in rice with superior yield under low N availability. Synergism among root phenes was an important component of phenotypic performance.
Intensive systems with two or three rice (
L.) crops per year account for about 50% of the harvested area for irrigated rice in Asia. Any reduction in productivity or sustainability of these systems ...has serious implications for global food security. Rice yield trends in the world's longest-running long-term continuous cropping experiment (LTCCE) were evaluated to investigate consequences of intensive cropping and to draw lessons for sustaining production in Asia. Annual production was sustained at a steady level over the 50-y period in the LTCCE through continuous adjustment of management practices and regular cultivar replacement. Within each of the three annual cropping seasons (dry, early wet, and late wet), yield decline was observed during the first phase, from 1968 to 1990. Agronomic improvements in 1991 to 1995 helped to reverse this yield decline, but yield increases did not continue thereafter from 1996 to 2017. Regular genetic and agronomic improvements were sufficient to maintain yields at steady levels in dry and early wet seasons despite a reduction in the yield potential due to changing climate. Yield declines resumed in the late wet season. Slower growth in genetic gain after the first 20 y was associated with slower breeding cycle advancement as indicated by pedigree depth. Our findings demonstrate that through adjustment of management practices and regular cultivar replacement, it is possible to sustain a high level of annual production in irrigated systems under a changing climate. However, the system was unable to achieve further increases in yield required to keep pace with the growing global rice demand.
•We reviewed the literature to assess the methods used to estimate rice yield gaps.•Current estimates are often difficult to interpret and compare.•Mean of top decile minus mean farm yield is ...relevant to estimate exploitable yield gaps.•To enable accurate interpretation, recommendations for future studies are proposed.•Rice yield gap studies should consider region, season and crop ecosystem as a minimum.
The important contribution of rice to global food security requires an understanding of yield gaps in rice-based farming systems. However, estimates of yield gaps are often compromised by a failure to recognize the components that determine them at a local scale. It is essential to define yield gaps by the biological limitations of the genotype and the environment. There exist a number of methods for estimating rice yield gaps, including the use of crop growth simulation models, field experiments and farmer yields. We reviewed the existing literature to (i) assess the methods used to estimate rice yield gaps at a local scale and to summarize the yield gaps estimated in those studies, (ii) identify practical methods of analysis that provides realistic estimates of exploitable rice yield gaps, and (iii) provide recommendations for future studies on rice yield gaps that will allow accurate interpretation of available data at a local level.
Rice yield gap analysis can be simplified without sacrificing precision and context specificity. This review identifies the comparison of the attainable farm yield (the mean of the top decile) with the population mean, as a practical and robust approach to estimate an exploitable yield gap that is highly relevant at the local level, taking into account what is achievable given the local socio-economic conditions. With this method we identified exploitable yield gaps ranging from 23 to 42% for one particular season in four different rice growing areas in Southeast Asia. To enable accurate estimation and interpretation of yield gaps in rice production systems, we propose a minimum dataset needed for rice yield gap assessment. Future studies on rice yield gaps should consider the region, season and crop ecosystem (e.g. upland rainfed, lowland irrigated) as a minimum to facilitate decisions at a local level. In addition, we recommend taking into account the cultivar, soil type, planting date, crop establishment method and nitrogen application rates, as well as field topography and toposequence for rainfed systems. A good understanding of rice yield gaps and the factors leading to yield gaps will allow better targeting of agricultural research and development priorities for livelihood improvement and sustainable rice production.
•First crop modeling framework of ORYZA2000 for the selection of drought resistant rice.•Multiple environment trials are consistently expanded to large environments by modeling.•Increase in the ...number of environment enhances genotypic effect and heritability.•Different genotypes can be selected to match breeding targets with modeling outputs.•ORYZA2000 is effective and highly repeatable tool to aid rice breeding.
Breeding line selection with conventional field methods is limited by time, cost, and appropriate environments. Crop models can be used as a tool to assist in breeding line selection by extrapolating the results of multiple-environment trials (MET) to large environments in a cost-effective and faster manner. This study is the first attempt to use ORYZA2000 for the selection of drought-resistant rice genotypes, and it provides a ‘virtual’ platform for a large number of environmental trials. In a case study, ORYZA2000 results from two field experiments in two environments were extrapolated to 669 environments in South Asia. For these two field experiments, the differences between simulated and field-measured grain yield and total above-ground biomass for all the 69 genotypes were within the standard deviations of the field measurements. This result confirmed that ORYZA2000 has the capability to correctly represent the growth and yield of rice genotypes under different environments. Using simulation outputs for 69 genotypes in 669 environments, the performance of these genotypes was evaluated for rainfed conditions with various drought stress. With the increase in the number of environments, the effect of the genotype on phenotypic performances across environments become much more significant than that of the effects of environment and genotype–environment interactions, and heritability was also increased. Desirable rice genotypes could then be selected by breeders based on the expected yield and adaptability to various environments generated by the model. The evaluation of rice genotypic performance by ORYZA2000, as ‘virtual’ multiple-environment trials, can improve the reliability of selected genotypes for a wide range of environments and enhance efficiency in terms of time consumption and cost effectiveness of the breeding process.
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