Modelling impacts of climate and weather extremes on wheat cropping systems across New South Wales

University of Technology Sydney. Faculty of Science.Australian wheat production is crucial to global food security, as Australia is one of the world’s major grain exporters. The NSW wheat belt is a main wheat production area in south-eastern Australia. Interannual wheat yields in the NSW wheat belt are highly variable, as the rainfed wheat cropping systems are significantly affected by recurrent climate and weather extremes. Ongoing climate change is projected to induce more extremes events, thereby leading to more unfavourable climate conditions for wheat production. This thesis aims to quantify the impacts of various climate and weather extremes on wheat yield in the present and explore their potential impacts in the future, thereby enhancing the capability of stakeholders to reduce yield losses. Five inter-related studies based on statistical regression-based models, process-based crop models, or the integration of both models were conducted in the NSW wheatbelt. Consistent findings demonstrate that: (1) Inter-annual variability of rainfall in winter and spring was largely responsible for wheat yield variation. (2) Seasonal agricultural drought conditions could be well monitored for the wheat belt using remote sensing information and machine learning-based statistical models. (3) APSIM simulated biomass, multiple climate extremes indices, NDVI, and SPEI were incorporated into the RF model to develop a hybrid model for improved modelling of impacts of climate extremes. Drought events throughout the growing season were identified as the main factor causing yield losses. (4) The wheat belt was expected to experience drier conditions in spring and winter but had little change in summer and autumn. By the end of the 21st century, over half of the wheat belt was at a high risk of experiencing spring and winter drought. (5) The hybrid model was used to assess the impacts of future climate and weather extremes on wheat yield. Increasing drought and heat events around reproductive stages were identified to be major threats causing yield losses in the future. This project enhanced systematic understanding of impacts of present and future climate and weather extremes on wheat yield and their likely changes in the future. However, certain aspects such as new crop cultivars, efficient management practices, pests and weed, were not explicitly considered in the modelling methods. Therefore, these findings should be further reconfirmed by models involving more influential information to guide agricultural production

Creating New Near-Surface Air Temperature Datasets to Understand Elevation-Dependent Warming in the Tibetan Plateau

The Tibetan Plateau has been undergoing accelerated warming over recent decades, and is considered an indicator for broader global warming phenomena. However, our understanding of warming rates with elevation in complex mountain regions is incomplete. The most serious concern is the lack of high-quality near-surface air temperature (Tair) datasets in these areas. To address this knowledge gap, we developed an automated mapping framework for the estimation of seamless daily minimum and maximum Land Surface Temperatures (LSTs) for the Tibetan Plateau from the existing MODIS LST products for a long period of time (i.e., 2002–present). Specific machine learning methods were developed and linked with target-oriented validation and then applied to convert LST to Tair. Spatial variables in retrieving Tair, such as solar radiation and vegetation indices, were used in estimation of Tair, whereas MODIS LST products were mainly focused on temporal variation in surface air temperature. We validated our process using independent Tair products, revealing more reliable estimates on Tair; the R2 and RMSE at monthly scales generally fell in the range of 0.9–0.95 and 1–2 °C. Using these continuous and consistent Tair datasets, we found temperature increases in the elevation range between 2000–3000 m and 4000–5000 m, whereas the elevation interval at 6000–7000 m exhibits a cooling trend. The developed datasets, findings and methodology contribute to global studies on accelerated warming

Extreme fire weather is the major driver of severe bushfires in southeast Australia

In Australia, the proportion of forest area that burns in a typical fire season is less than for other vegetation types. However, the 2019–2020 austral spring-summer was an exception, with over four times the previous maximum area burnt in southeast Australian temperate forests. Temperate forest fires have extensive socio-economic, human health, greenhouse gas emissions, and biodiversity impacts due to high fire intensities. A robust model that identifies driving factors of forest fires and relates impact thresholds to fire activity at regional scales would help land managers and fire-fighting agencies prepare for potentially hazardous fire in Australia. Here, we developed a machine-learning diagnostic model to quantify nonlinear relationships between monthly burnt area and biophysical factors in southeast Australian forests for 2001–2020 on a 0.25° grid based on several biophysical parameters, notably fire weather and vegetation productivity. Our model explained over 80% of the variation in the burnt area. We identified that burnt area dynamics in southeast Australian forest were primarily controlled by extreme fire weather, which mainly linked to fluctuations in the Southern Annular Mode (SAM) and Indian Ocean Dipole (IOD), with a relatively smaller contribution from the central Pacific El Nino Southern Oscillation (ENSO). Our fire diagnostic model and the non-linear relationships between burnt area and environmental covariates can provide useful guidance to decision-makers who manage preparations for an upcoming fire season, and model developers working on improved early warning systems for forest fires

Clarifying confusions over carbon conclusions: antecedent soil carbon drives gains realised following intervention

Carbon removals associated with incremental gains in soil organic carbon (SOC) at scale have enormous potential to mitigate global warming, yet confusion over contexts that elicit SOC accrual abound. Here, we examine how bespoke interventions (through irrigation, fertiliser, crop type and rotations), antecedent SOC levels and soil type impact on long-term SOC accrual and greenhouse gas (GHG) emissions. Using a whole farm systems modelling approach informed using participatory research, we discovered an inverse relationship between antecedent SOC stocks and SOC gains realised following intervention, with greater initial SOC levels resulting in lower ex poste change in SOC. We found that SOC accrual was greatest for clays and least for sands, although changes in SOC in sandy loam soils were also low. Diversified whole farm adaptations – implemented through inclusion of grain legumes within wheat/canola crop rotations – were more conducive to improvement in SOC stocks, followed by Intensified systems (implemented through greater rates of irrigation, farm areas under irrigation, nitrogen fertiliser and inclusion of rice and maize in crop rotations). Adaptations that Simplified farm systems by reducing irrigation and fertiliser use resulted in the lowest SOC accrual. In most cases, long-term SOC stocks fell when SOC at the outset was greater than 4–5%, regardless of intervention made, soil or crop type, crop rotation, production system or climate. We contend that (1) management interventions primarily impacted SOC in the soil surface (0–30 cm) and had de minimus impact on deep SOC stocks (30–100 cm), (2) crop rotations including wheat, canola and faba beans were more conducive to improvement in SOC stocks, (3) scenarios with high status quo SOC had little impact on crop productivity, and not necessarily the lowest GHG emissions intensity, (4) productivity and GHG emissions intensity were largely a function of the quantum of nitrogenous fertiliser added, rather than SOC stocks, and (5) aspirations for improving SOC are likely to be futile if antecedent SOC stocks are already high (4–5 %). We conclude that potential for improving SOC stocks exists in contexts where antecedent stocks are low (<1%), which may include regions with land degradation, chronic erosion and/ or other constraints to vegetative ground cover that could be sustainably and consistently alleviated

Sustainable intensification with irrigation raises farm profit despite climate emergency

Societal Impact Statement Despite comprising a small proportion of global agricultural land use, irrigated agriculture is enormously important to the global agricultural economy. Burgeoning food demand driven by population growth—together with reduced food supply caused by the climate crisis—is polarising the existing tension between water used for agricultural production versus that required for environmental conservation. We show that sustainable intensification via more diverse crop rotations, more efficient water application infrastructure and greater farm area under irrigation is conducive to greater farm business profitability under future climates. Summary &bull; Research aimed at improving crop productivity often does not account for the complexity of real farms underpinned by land-use changes in space and time. &bull; Here, we demonstrate how a new framework—WaterCan Profit—can be used to elicit such complexity using an irrigated case study farm with four whole-farm adaptation scenarios (Baseline, Diversified, Intensified and Simplified) with four types of irrigated infrastructure (Gravity, Pipe & Riser, Pivot and Drip). &bull; Without adaptation, the climate crisis detrimentally impacted on farm profitability due to the combination of increased evaporative demand and increased drought frequency. Whole-farm intensification—via greater irrigated land use, incorporation of rice, cotton and maize and increased nitrogen fertiliser application—was the only adaptation capable of raising farm productivity under future climates. Diversification through incorporation of grain legumes into crop rotations significantly improved profitability under historical climates; however, profitability of this adaptation declined under future climates. Simplified systems reduced economic risk but also had lower long-term economic returns. &bull; We conclude with four key insights: (1) When assessing whole-farm profit, metrics matter: Diversified systems generally had higher profitability than Intensified systems per unit water, but not per unit land area; (2) gravity-based irrigation infrastructure required the most water, followed by sprinkler systems, whereas Drip irrigation used the least water; (3) whole-farm agronomic adaptation through management and crop genotype had greater impact on productivity compared with changes in irrigation infrastructure; and (4) only whole-farm intensification was able to raise profitability under future climates

Simulating the Effects of Different Textural Soils and N Management on Maize Yield, N Fates, and Water and N Use Efficiencies in Northeast China

Determining the best management practices (BMPs) for farmland under different soil textures can provide technical support for improving maize yield, water- and nitrogen-use efficiencies (WUE and NUE), and reducing environmental N losses. In this study, a two-year (2013–2014) maize cultivation experiment was conducted on two pieces of farmland with different textural soils (loamy clay and sandy loam) in the Phaeozems zone of Northeast China. Three N fertilizer treatments were designed for each farmland: N168, N240, and N312, with N rates of 168, 240, and 312 kg ha−1, respectively. The WHCNS (soil Water Heat Carbon Nitrogen Simulator) model was calibrated and validated using the observed soil water content, soil nitrate concentration, and crop biological indicators. Then, the effects of soil texture combined with different N rates on maize yield, water consumption, and N fates were simulated. The integrated index considering the agronomic, economic, and environmental impacts was used to determine the BMPs for two textural soils. Results indicated that simulated soil water content and nitrate concentration at different soil depths, leaf area index, dry matter, and grain yield all agreed well with the measured values. Both soil texture and N rates significantly affected maize yield, N fates, WUE, and NUE. The annual average grain yield, WUE, and NUE under three N rates in sandy loam soil were 8257 kg ha−1, 1.9 kg m−3, and 41.2 kg kg−1, respectively, which were lower than those of loam clay, 11440 kg ha−1, 2.7 kg m−3, and 46.7 kg kg−1. The order of annual average yield and WUE under two textural soils was N240 > N312 > N168. The average evapotranspiration of sandy loam (447.3 mm) was higher than that of loamy clay (404.9 mm). The annual average N-leaching amount of different N treatments for sandy loam ranged from 5.1 to 13.2 kg ha−1, which was higher than that of loamy clay soil, with a range of 1.8–5.0 kg ha−1. The gaseous N loss in sandy loam soil accounted for 14.7% of the fertilizer N application rate, while it was 11.1%in loamy clay soil. The order of the NUEs of two textural soils was: N168 > N240 > N312. The recommended N fertilizer rates for sandy loam and loamy clay soils determined by the integrated index were 180 and 200 kg ha−1, respectively

Effects of the Ratio of Substituting Mineral Fertilizers with Manure Nitrogen on Soil Properties and Vegetable Yields in China: A Meta-Analysis

Substituting mineral fertilizers (MFs) with manure nitrogen (N) can not only reduce environmental pollution, but also improve soil quality. However, the effects of various manure N substitution ratios (SRs, the ratio of manure N over total N applied) on soil properties and vegetable yields in China are poorly studied. Here, through a meta-analysis of 667 observations, we assessed the effects of three manure N SRs (low (SR ≤ 35%), medium (35% 70%)) on vegetable yields and soil properties (soil organic carbon, SOC; soil total nitrogen, STN; microbial biomass carbon (C) and nitrogen (N), MBC/N; and available phosphorus and potassium, (AP/AK)) in the 0–20 cm soil under different climatic conditions, initial soil properties, and management practices. The results show that the SOC and STN contents increased by 28.5% and 21.9%, respectively, under the medium SRs compared to the MF, which were the highest among the three SRs. Both soil MBC and MBN increased with the increase in the SRs, and the increased ratios in the high SRs reached 203.4% and 119.3%, respectively. In addition, the AP also increased with the increase in the SR, but the AK was not significantly changed with the low and medium SRs compared with the MF. Overall, the medium SR produced the highest vegetable yield among the three SRs with an increase of 18.6%. Additionally, a random forest analysis indicated that the N application rate, planting years, and mean annual precipitation were the most important factors influencing vegetable yield. In conclusion, the SR of 35–70% is more conducive to increasing soil nutrient contents significantly and improves vegetable yields in Chinese vegetable fields

Improvement of Water and Nitrogen Use Efficiencies by Alternative Cropping Systems Based on a Model Approach

The conventional double cropping system of winter wheat and summer maize (WW-SUM) in the North China Plain (NCP) consumes a large amount of water and chemical fertilizer, threatening the sustainable development of agriculture in this region. This study was based on a three-year field experiment of different cropping systems (2H1Y&mdash;two harvests in one year; 3H2Y&mdash;three harvests in two years; and 1H1Y&mdash;one harvest in one year). The 2H1Y system had three irrigation&ndash;fertilization practices (FP&mdash;farmer&rsquo;s practice; RI&mdash;reduced input; and WQ&mdash;Wuqiao pattern in Wuqiao County, Hebei Province). A soil&ndash;crop system model (WHCNS&mdash;soil water heat carbon nitrogen simulator) was used to quantify the effects of different cropping systems on water and nitrogen use efficiencies (WUE and NUE, respectively), and to explore the trade-offs between crop yields and environmental impacts. The results showed that annual yield, water consumption, and the WUE of 2H1Y were higher than those of the 3H2Y and 1H1Y systems. However, local precipitation during the period of crop growth could only meet 65%, 76%, and 91% of total water consumption for the 2H1Y, 3H2Y and 1H1Y systems, respectively. Nearly 65% of irrigation water (groundwater) was used in the period of wheat growth that contributed to almost 40% of the annual yield. Among the three patterns of the 2H1Y system, the order of the WUE was 2H1Y_RI &gt; 2H1Y_WQ &gt; 2H1Y_FP. Compared to 2H1Y_FP, the total fertilizer N application rates in 2H1Y_WQ, 2H1Y_RI, and 3H2Y were reduced by 25%, 65%, and 74%, respectively. The 3H2Y system had the highest NUE of 34.3 kg kg&minus;1, 54% greater than the 2H1Y_FP system (22.2 kg kg&minus;1). Moreover, the 3H2Y system obviously reduced nitrate leaching and gaseous N loss when compared with the other two systems. The order of total N loss of different cropping systems was 2H1Y (261 kg N ha&minus;1) &gt; 1H1Y (78 kg N ha&minus;1) &gt; 3H2Y (70 kg N ha&minus;1). Considering the agronomic and environmental effects as well as economic benefits, the 3H2Y cropping system with optimal irrigation and fertilization would be a promising cropping system in the NCP that could achieve the balance between crop yield and the sustainable use of groundwater and N fertilizer

Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield

Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change

Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield

Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change