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【Objective】 Longzhong is a region located over the transitional zone between the Sichuan Basin and the Loess Plateau; it has a unique climate and agriculture. This paper investigates the interactive impact of meteorological factors and nitrogen fertilization on yield of spring wheat in dryland in the region. 【Method】 The study was based on the APSIM model, using meteorological data measured from 1970 to 2017, crop growth data measured from 2016 to 2017 in zero-till mulched fields, and grain yield data measured between 2005—2009 and 2013—2015. We simulated the response of grain yield of the spring wheat to changes in next-day average temperature, average daily radiation, CO2 mass fraction, nitrogen fertilization, both individually or in combination. 【Result】 ① The simulated growing duration and grain yield agreed well with the ground-true data with R2=0.98 and NRMSE=5% for the former; and R2=0.91, NRMSE=12% and D=0.95 for the latter. ② For the three precipitation scenarios we simulated, changing meteorological factors and nitrogen fertilization individually or in combination both had a significant impact on grain yield, especially in dry years. ③ When other conditions were the same, grain yield reached its peak and showed greater stability during wet years. 【Conclusion】 Elevated precipitation not only boosted grain yield but also played a pivotal role in influencing the effects of other meteorological factors on crop growth and enhancing the efficiency of nitrogen fertilizer.

The impact of global climate change is a challenge to the sustainability of many ecosystems, including soil systems. However, the performance of soil properties under future climate was rarely assessed. Therefore, this study was carried out to evaluate selected soil processes under climate change using an agri-environmental modeling approach to Sri Lanka. The Agricultural Production Systems Simulator (APSIM) model was used to simulate soil and plant-related processes using recent past (1990–2019) and future (2041–2070) climates. Future climate data were obtained for a regional climate model (RCM) under representative concentrations pathway 4.5 scenarios. Rainfalls are going to be decreased in all the tested locations under future climate scenarios while the maximum temperature showcased rises. According to simulated results, the average yield reduction under climate change was 7.4%. The simulated nitrogen content in the storage organs of paddy declined in the locations (by 6.4–25.5%) as a reason for climate change. In general, extractable soil water relative to the permanent wilting point (total available water), infiltration, and biomass carbon lost to the atmosphere decreased while soil temperature increased in the future climate. This modeling approach provides a primary-level prediction of soil dynamics under climate change, which needs to be tested using fieldwork. [ABSTRACT FROM AUTHOR]

The impact of climate change on crop production is a major concern in drought-prone regions, which are experiencing increasingly severe drought conditions. The goal of this study was to use the Agricultural Production System Simulator (APSIM) model to simulate and predict flax yield and water balance, as well as to determine the optimal irrigation and fertilizer for flax production to counteract the effects of climate change under arid and semiarid conditions. The model was calibrated using field experimental data from 2019 to 2020 and evaluated using field experimental data from 2021 to 2022 with a combination of four irrigation treatments (full irrigation, 180 mm, deficit irrigation at vegetative and reproductive stage, no irrigation) and four fertilizer rates (no fertilizer, NPK, NPK + flax oil residue, NPK + farm manure) using a plot design for a total of 16 treatments. To determine the key irrigation and fertility periods and irrigation and fertilization amounts that affect flax yield to address climate change, a combination of four irrigation and six fertilizer rates and six irrigation stages were simulated. The results showed that the model successfully predicted flax yield (R2 = 0.98) and water-use efficiency (WUE) (R2 = 0.79). When compared to inorganic fertilization, the grain yield and WUE improved by 16.47% and 13.83%; replacing 50% of inorganic fertilizer with flax oil residue achieved the optimal results. The flax yield and WUE increased by 3.37% and 1.25% under full irrigation (180 mm) compared to irrigation of 120 mm with a not-very-significant difference. The positive effect of irrigation on soil water content (SWC) was highest during the budding stage, followed by the flowering stage, fruiting stage, and stemming stage. Therefore, in arid and semiarid areas with scarce water resources, irrigation at a 55% deficiency during the vegetative growth period of flax combined with the application of flax oil residue and NPK (1550 flax oil residue, 45 N, 50.2 P2O5, and 33.9 K2O kg ha−1) might be an effective adaptation strategy for improved future flax production. Our results can facilitate the development of sustainable agriculture practices that reduce water input and improve WUE to counteract climate change effects. [ABSTRACT FROM AUTHOR]

It is difficult to identify farm management practices that consistently provide greenhouse gas (GHG) abatement at different locations because effectiveness of practices is greatly influenced by climates and soils. We address this knowledge gap by identifying practices that provide abatement in eight case studies located across diverse conditions in Australian's grain-producing areas. The case studies focus on soil-based emissions of nitrous oxide (N2O) and changes in soil organic carbon (SOC), simulated over 100 years for 15 cropping management scenarios. Average changes in the balance of GHG from both N2O emissions and SOC sequestration (∆GHG balance) and gross margins compared to a high emissions baseline were determined over 25 and 100 simulated years. Because scenarios providing the greatest abatement varied across individual case studies, we aggregated the data over all case studies and analysed them with a random forest data mining approach to build models for predicting ∆GHG balance. Increased cropping intensity, achieved by including cover crops, additional grains crops, or crops with larger biomass in the rotation, was the leading predictor of ∆GHG balance across the scenarios and sites. Abatement from increased cropping intensity averaged 774 CO2-e ha−1 year−1 (25 years) and 444 kg CO2-e ha−1 year−1 (100 years) compared to the baseline, with reduced emissions from SOC sequestration offsetting increased N2O emissions for both time frames. Increased cropping intensity decreased average gross margins, indicating that a carbon price would likely be needed to maximise GHG abatement from this management. To our knowledge, this is the first time that the random forest approach has been applied to assess management practice effectiveness for achieving GHG abatement over diverse environments. Doing so provided us with more general information about practices that provide GHG abatement than would have come from qualitative comparison of the variable results from the case studies. [ABSTRACT FROM AUTHOR]

Heat stress in combination with drought has become the biggest concern and threat for maize yield production, especially in arid and hot regions. Accordingly, different optimal solutions should be considered in order to maintain maize production and reduce the risk of heat stress under the changing climate. In the current study, the risk of heat stress across Iranian maize agro-ecosystems was analyzed in terms of both intensity and frequency. The study areas comprised 16 provinces and 24 locations classified into five climate categories: arid and hot, arid and temperate, semi-arid and hot, semi-arid and temperate, and semi-arid and cold. The impact of heat stress on maize under a future climate was based on a 5‐multi‐model ensemble under two optimistic and pessimistic emission scenarios (RCP4.5 and RCP8.5, respectively) for 2040–2070 using the APSIM crop model. Simulation results illustrated that in the period of 2040–2070, intensity and the frequency of heat stress events increased by 2.37 °C and 79.7%, respectively, during maize flowering time compared to the baseline. The risk of heat stress would be almost 100% in hot regions in the future climate under current management practices, mostly because of the increasing high-risk window for heat stress which will result in a yield reduction of 0.83 t ha−1. However, under optimal management practices,farmers will economically obtain acceptable yields (6.6 t ha−1). The results also indicated that the high-risk windows in the future will be lengthening from 12 to 33 days in different climate types. Rising temperatures in cold regions as a result of global warming would provide better climate situations for maize growth, so that under optimistic emission scenarios and optimal management practices, farmers will be able to boost grain yield up to 9.2 t ha−1. Overall, it is concluded that farmers in hot and temperate regions need to be persuaded to choose optimal sowing dates and new maize cultivars which are well adapted to each climate to reduce heat stress risk and to shift maize production to cold regions. [ABSTRACT FROM AUTHOR]

Simple Summary: Adopting deficit irrigation (DI) to improve crop production and safeguard groundwater resources is of great importance in water scarce regions, e.g., the North China Plain (NCP). Under the background of global warming, it is worth investigating whether DI continues to play such a key role under future climate change scenarios. Thus, we studied the effect of DI on wheat yield and water use efficiency under future climate change scenarios. We found that moderate deficit irrigation (DI3, ≤0.4 PAWC at sowing to flowering stage) under the N3 (150 kg N ha−1) condition was identified as the optimum irrigation schedule for the study site under future climate change scenarios. However, the compensation effect of DI3 on yield and water use efficiency (WUE) became weak in the future. To conclude, in water scarce regions of NCP, DI remains an effective strategy to maintain higher yield and enhance water use under future climate scenarios. Background. Deficit irrigation (DI) is a feasible strategy to enhance crop WUE and also has significant compensation effects on yield. Previous studies have found that DI has great potential to maintain crop production as full irrigation (FI) does. Therefore, adopting DI to improve crop production and safeguard groundwater resources is of great importance in water scarce regions, e.g., the North China Plain (NCP). Under the background of global warming, it is worth investigating whether DI continues to play such a key role under future climate scenarios. Methods. We studied the response of winter wheat yield and WUE to different DI levels at pre-anthesis under two Shared Socioeconomic Pathways (SSPs) scenarios (SSP245 and SSP585) using the Agricultural Production Systems Simulator (APSIM) model driven by 21 general circulation models (GCMs) from the Coupled Model Inter-Comparison Project phase 6 (CMIP6). Additionally, we explored the effects of different nitrogen (N) fertilizer application rates on DI. Results. We found that simulated wheat yield would increase by 3.5–45.0%, with WUE increasing by 8.8–46.4% across all treatments under future climate change. Moderate deficit irrigation (DI3, ≤0.4 PAWC at the sowing to flowering stage) under the N3 (150 kg N ha−1) condition was identified as the optimum irrigation schedule for the study site under future climate change. However, compensation effects of DI3 on yield and WUE became weak in the future, which was mainly due to increased growing season rainfall projected by GCMs. In addition, we found that N fertilizer application could mitigate the effect of DI3. Conclusions. We highlight that in water scarce regions of NCP, DI remains an effective strategy to maintain higher yield and enhance water use under future climate scenarios. Results strongly suggest that moderate deficit irrigation under a 150 kg N ha−1 condition could mitigate the contradiction between production and water consumption and ensure food safety in the NCP. [ABSTRACT FROM AUTHOR]

Cultivar and sowing date selection are major factors in determining the yield potential of any crop and in any region. To explore how climate change affects these choices, this study performed a regional scale analysis using the well-validated APSIM-maize model for the Northeast China Plain (NEC) which is the leading maize (Zea mays L.) producing area in China. Results indicated that high temperature had a significantly negative effect on grain yield, while effective accumulated temperature and solar radiation had significant positive effects on grain yield and kernel number. Cloudy and rainy weather in flowering stage had significant negative effects on kernel number. Delayed sowing led to less cloudy and rainy weather during flowering and reduced the negative effect on kernel number. Higher diurnal thermal range and less precipitation during the grain-filling stage also increased the 1000-kernel weight. Delayed sowing, however, also significantly increased the risk of early senescence and frost (>80%) in middle and high latitude areas. In the middle and high latitude areas of the NEC, the grain yield of a long-season cultivar (LS) under early sowing (I) (6.2–19.9%) was significantly higher than under medium sowing (II) or late sowing (III), and higher than that of an early sown (I) short-season (SS) and medium-season cultivar (MS). In the low latitude area of the NEC, the grain yield of MS under medium sowing date (II) was higher than that under I and III, meanwhile, this was also higher than that of SS and LS. Therefore, under climate warming, LS sown earlier in high and medium latitudes and MS sown medium in low latitude were the appropriate cultivar and sowing date choices, which could mitigate the stress of high temperatures and reduce the risk of early senescence and frost. Cultivar and sowing date selection are effective measures to alleviate negative effects of climate change on maize production in the NEC, and provides valuable advice for breeders on cultivar selection, and the choice of varieties and sowing dates for farmers in actual production. [ABSTRACT FROM AUTHOR]

Abstract: Understanding the confounding impacts of climatic and varietal changes on crop phenology is of importance to help make appropriate adaptation measures and target future directions for crop breeding. This paper investigated the changes in winter wheat phenology and analysed the impact of climatic, varietal and sowing date changes on crop phenology during the last three decades by combining field data with modeling. The APSIM crop model was used to quantify the changes in wheat phenology in terms of vernalization and photoperiod sensitivity as well as the changes in thermal time of pre- and post-flowering stage among wheat varieties at six sites in the North China Plain (NCP). The results showed that APSIM model could capture the phenological changes of winter wheat caused by interannual climatic change. There was a large spatial difference in the response pattern of wheat phenology to climatic and varietal changes across NCP. At most sites the increase in temperature shortened the growth duration of winter wheat mainly by shortening the growth period from sowing to jointing. The delayed sowing of winter wheat further shortened the growth period from sowing to jointing at two northern sites (Tianjin and Huimin). Continuous adoption of new wheat varieties shortened the growth period from sowing to jointing at Huimin, Xinxiang and Zhumadian, from jointing to flowering at Tianjin, but helped increase the length of growth period from jointing to flowering at Huimin, Tangyin and Xinxiang and post-flowering growth period at all the sites except for Tangyin. The findings of the study suggest that past cultivar changes in wheat in the NCP have been varied geographically. [Copyright &y& Elsevier]

Abstract: Long-term soil carbon (C) dynamics in agro-ecosystems is controlled by interactions of climate, soil and agronomic management. A modeling approach is a useful tool to understand the interactions, especially over long climatic sequences. In this paper, we examine the performance of the Agricultural Production Systems sIMulator (APSIM) to predict the long-term soil C dynamics under various agricultural practices at four semi-arid sites across the wheat-belt of eastern Australia. We further assessed the underlying factors that regulate soil C dynamics in the top 30cm of soil through scenario analysis using the validated model. The results show that APSIM is able to predict aboveground biomass production and soil C dynamics at the study sites. Scenario analyses indicate that nitrogen (N) fertilization combined with residue retention (SR) has the potential to significantly slow or reverse the loss of C from agricultural soils. Optimal N fertilization (Nopt) and 100% SR, increased soil C by 13%, 46% and 45% at Warra, Wagga Wagga and Tarelee, respectively. Continuous lucerne pasture was the most efficient strategy to accumulate soil C, resulting in increases of 49%, 57% and 50% at Warra, Wagga Wagga and Tarlee, respectively. In contrast, soil C decreases regardless of agricultural practices as a result of cultivation of natural soils at the Brigalow site. Soil C input, proportional to the amount of retained residue, is a significant predictor of soil C change. At each site, water and nitrogen availability and their interaction, explain more than 59% of the variation in soil C. Across the four sites, mean air temperature has significant (P <0.05) effects on soil C change. There was greater soil C loss at sites with higher temperature. Our simulations suggest that detailed information on agricultural practices, land use history and local environmental conditions must be explicitly specified to be able to make plausible predictions of the soil C balance in agro-ecosystems at different agro-ecological scales. [Copyright &y& Elsevier]

Abstract: In the North China Plain (NCP), while irrigation using groundwater has maintained a high-level crop productivity of the wheat–maize double cropping systems, it has resulted in rapid depletion of groundwater table. For more efficient and sustainable utilization of the limited water resources, improved understanding of how crop productivity and water balance components respond to climate variations and irrigation is essential. This paper investigates such responses using a modelling approach. The farming systems model APSIM (Agricultural Production Systems Simulator) was first calibrated and validated using 3 years of experimental data. The validated model was then applied to simulate crop yield and field water balance of the wheat–maize rotation in the NCP. Simulated dryland crop yield ranged from 0 to 4.5tha−1 for wheat and 0 to 5.0tha−1 for maize. Increasing irrigation amount led to increased crop yield, but irrigation required to obtain maximum water productivity (WP) was much less than that required to obtain maximum crop yield. To meet crop water demand, a wide range of irrigation water supply would be needed due to the inter-annual climate variations. The range was simulated to be 140–420mm for wheat, and 0–170mm for maize. Such levels of irrigation applications could potentially lead to about 1.5myear−1 decline in groundwater table when other sources of groundwater recharge were not considered. To achieve maximum WP, one, two and three irrigations (i.e., 70, 150 and 200mmseason−1) were recommended for wheat in wet, medium and dry seasons, respectively. For maize, one irrigation and two irrigations (i.e., 60 and 110mmseason−1) were recommended in medium and dry seasons, while no irrigation was needed in wet season. [Copyright &y& Elsevier]

Climate change has likely impacted crop yield potential in major rain-fed crop-growing regions. However, the impact on the spatial pattern across regions is unclear. Here, the wheat belt of Western Australia was used as a case study to investigate the effect of historical climate change on the spatial patterns of water-limited crop yield. We used 117 years (1900–2016) of observed daily climate data on ~ 5 km × ~ 5 km grids to map and quantify the spatial-temporal changes in water-limited wheat yield simulated by the APSIM model. The climate data were split into four periods based on distinct changes in rainfall (Period 1, 1900–1934; Period 2, 1935–1974; Period 3, 1975–1999; and Period 4, 2000–2016). The results showed that the observed decreases in rainfall shifted the regional wheat yield potential towards the southwest of the wheat belt by an average of 70 km between the first and last periods. Observed increases in CO2 counteracted this by about half of this movement. Actual wheat yields achieved by farmers have not decreased, thanks to improvements in crop genetics and management, but the simulated decrease in water-limited yields has meant that actual yields in this region are not as high as they might have been. Future climate change is likely to continue to impact on water-limited crop yield and its spatial pattern in Western Australia. Cropping systems will need to continually evolve to cope with a changing climate, and every aspect of agronomy and genetics needs to be considered. Without continuing improvements, there will likely be a decrease in wheat yield across this cropping region. [ABSTRACT FROM AUTHOR]

The potential impacts of climate change on wheat were assessed for Kulmsa area in central highlands of Ethiopia using the Agricultural Production Systems sIMulator (APSIM)—wheat model. The objectives were to (i) evaluate the performance of wheat under increased temperatures with or without changes in rainfall and carbon dioxide (CO2) levels and (ii) assess the response of different wheat cultivars to projected future climates under improved management practices (optimal nitrogen rate, planting date, and density) (IMPs). The model was first calibrated and used to identify ranges of IMPs. Then, the model was used to (i) conduct sensitivity analysis of wheat in response to assumed elevated temperature (T), with and without change in rainfall and CO2 level, and to (ii) evaluate average effect of future worst-case climate change scenarios as predicted by an ensemble of three global climate models (GCM) under highest Representative Concentration Pathways (RCP8.5) for three future time frames, NF (2010–2039), MC (2040–2069), and EC (2070–2099). The simulation was evaluated for three wheat cultivars ("early", "medium", and "late") relative to their corresponding baseline values under the selected IMPs. The baseline climate was represented by the long-term (1980–2009) dataset and CO2 of 360 μmol/mol. Results showed that increased temperature above 4 °C alone had strong negative impacts on yields under the IMPs when CO2 staying constant at the baseline level. In contrast, climate change simulations with GCM projections under IMPs and elevated CO2 effect showed that wheat yield remained unchanged (− 0.4 to + 9%) for all three genotypes. This suggests that the IMPs and elevated CO2 were able to reduce the negative effect of elevated T on wheat yield as T stress did not go beyond optimal T range for wheat. Overall, climate change may not reduce wheat production in the climate of the location of study in the near future, midcentury, or end century. [ABSTRACT FROM AUTHOR]

Despite the detrimental impact that excess moisture can have on soybean (Glycine max [L.] Merr) yields, most of today's crop models do not capture soybean's dynamic responses to waterlogged conditions. In light of this, we synthesized literature data and used the APSIM software to enhance the modeling capacity to simulate plant growth, development, and N fixation response to flooding. Literature data included greenhouse and field experiments from across the U.S. that investigated the impact of flood timing and duration on soybean. Five datasets were used for model parameterization of new functions and three datasets were used for testing. Improvements in prediction accuracy were quantified by comparing model performance before and after the implementation of new stage-dependent excess water functions for phenology, photosynthesis and N-fixation. The relative root mean square error (RRMSE) for yield predictions improved by 26% and the RRMSE predictions of biomass improved by 40%. Extensive model testing found that the improved model accurately simulates plant responses to flooding including how these responses change with flood timing and duration. When used to project soybean response to future climate scenarios, the model showed that intense rain events had a greater negative effect on yield than a 25% increase in rainfall distributed over 1 or 3 month(s). These developments advance our ability to understand, predict and, thereby, mitigate yield loss as increases in climatic volatility lead to more frequent and intense flooding events in the future. [ABSTRACT FROM AUTHOR]

This study assessed the growth of wheat under climate change conditions (increases in CO2 and temperature) in southwestern Iran. Future climate data were projected using long‐term climate data for radiation and minimum and maximum temperatures for the baseline period 1980–2010. The future climate scenarios made use of delta changes from the Coupled Model Intercomparison Project Phase 5 (CMIP5) in the R package of the Agricultural Model Intercomparison and Improvement Project for 2040–2070. After generating the weather data, the Agricultural Production System Simulator wheat model was applied to assess the potential impact of climate change on wheat yield and the relative growth rate, crop growth rate, total dry weight and leaf area index. Averaged across all locations, an increase of 1.6 and 2.3°C in annual temperature was projected under representative concentration pathway 4.5 (RCP4.5) and RCP8.5, respectively. The decrease in the average length of the growing season was 7.5 and 9.3% under RCP4.5 and RCP8.5, respectively, in the mid‐21st century with respect to baseline. The mean cumulative radiation will decrease by 11.6 and 14.3% averaged across all sites for RCP4.5 and RCP8.5, respectively. Grain yield will decrease at all sites for both RCPs. The greatest decrease in wheat yield was projected for Izeh under RCP4.5 and RCP8.5 (7.9 and 9.3%, respectively) for 2040–2070 relative to baseline. A greater grain yield at baseline than for the RCP8.5 and RCP4.5 scenarios was closely related to higher leaf area index, relative growth rate and crop growth rate values during the reproductive phase. Thus, these parameters can be defined as indicators of crop yield. [ABSTRACT FROM AUTHOR]

Foreknowledge about sugarcane crop size can help industry members make more informed decisions. There exists many different combinations of climate variables, seasonal climate prediction indices, and crop model outputs that could prove useful in explaining sugarcane crop size. A data mining method like random forests can cope with generating a prediction model when the search space of predictor variables is large. Research that has investigated the accuracy of random forests to explain annual variation in sugarcane productivity and the suitability of predictor variables generated from crop models coupled with observed climate and seasonal climate prediction indices is limited. Simulated biomass from the APSIM (Agricultural Production Systems sIMulator) sugarcane crop model, seasonal climate prediction indices and observed rainfall, maximum and minimum temperature, and radiation were supplied as inputs to a random forest classifier and a random forest regression model to explain annual variation in regional sugarcane yields at Tully, in northeastern Australia. Prediction models were generated on 1 September in the year before harvest, and then on 1 January and 1 March in the year of harvest, which typically runs from June to November. Our results indicated that in 86.36 % of years, it was possible to determine as early as September in the year before harvest if production would be above the median. This accuracy improved to 95.45 % by January in the year of harvest. The R-squared of the random forest regression model gradually improved from 66.76 to 79.21 % from September in the year before harvest through to March in the same year of harvest. All three sets of variables-(i) simulated biomass indices, (ii) observed climate, and (iii) seasonal climate prediction indices-were typically featured in the models at various stages. Better crop predictions allows farmers to improve their nitrogen management to meet the demands of the new crop, mill managers could better plan the mill's labor requirements and maintenance scheduling activities, and marketers can more confidently manage the forward sale and storage of the crop. Hence, accurate yield forecasts can improve industry sustainability by delivering better environmental and economic outcomes. [ABSTRACT FROM AUTHOR]

El Niño–Southern Oscillation strongly influences rainfall and temperature patterns in Eastern Australia, with major impacts on frost, heat, and drought stresses, and potential consequences for wheat production. Wheat phenology is a key factor to adapt to the risk of frost, heat, and drought stresses in the Australian wheatbelt. This study explores broad and specific options to adapt wheat cropping systems to El Niño–Southern Oscillation, and more specifically, to the Southern Oscillation Index (SOI) phases ahead of the season (i.e., April forecast) in Eastern Australia, when wheat producers make their most crucial management decisions. Crop model simulations were performed for commercially-grown wheat varieties, as well as for virtual genotypes representing possible combinations of phenology alleles that are currently present in the Australian wheat germplasm pool. Different adaptation strategies were tested at the site level, across Eastern Australia, for a wide range of sowing dates and nitrogen applications over long-term historical weather records (1900–2016). The results highlight that a fixed adaptation system, with genotype maturities, sowing time, and nitrogen application adapted to each location would greatly increase wheat productivity compared to sowing a mid-maturity genotype, mid-season, using current practices for nitrogen applications. Tactical adaptation of both genotype and management to the different SOI phases and to different levels of initial Plant Available Water ('PAW & SOI adaptation') resulted in further yield improvement. Site long-term increases in yield and gross margin were up to 1.15 t·ha−1 and AU$ 223.0 ha−1 for fixed adaptation (0.78 t·ha−1 and AU$ 153 ha−1 on average across the whole region), and up to an extra 0.26 t·ha−1 and AU$ 63.9 ha−1 for tactical adaptation. For the whole eastern region, these results correspond to an annual AU$ 440 M increase for the fixed adaptation, and an extra AU$ 188 M for the PAW & SOI tactical adaptation. The benefits of PAW & SOI tactical adaptation could be useful for growers to adjust farm management practices according to pre-sowing seasonal conditions and the seasonal climate forecast. [ABSTRACT FROM AUTHOR]