Early and reliable seasonal crop yield forecasts are crucial for both farmers and decision-makers. Commonly-used methods for seasonal yield forecasting are based on process-based crop models or ...statistical regression-based models. Both have limitations, particularly in regard to accounting for growth stage-specific climate extremes (such as drought, heat, and frost). In this study, we firstly developed a hybrid yield forecasting approach by blending of multiple growth stage-specific indicators, i.e. APSIM (a process-based crop model)-simulated biomass, and climate extremes, NDVI (Normalized Difference Vegetation Index), and SPEI (Standardized Precipitation and Evapotranspiration Index) before forecasting dates, using a regression model (random forest or multiple linear regression). Plot-scale wheat yield (2008–2017) in the southeastern Australian wheat belt was dynamically forecasted at the end of several targeted growth stages as the growing season progressed to harvest. Results showed that the forecasting accuracy increased significantly for both systems as forecast time approached harvest time. The forecasting system based on random forest outperformed the forecasting system based on multiple linear regression at each forecasting event. Satisfactory yield forecasts occurred at one month (~35 days) prior to harvest (r = 0.85, LCCC = 0.81, MAPE = 17.6%, RMSE = 0.70 t ha−1, and ROC score = 0.90), and at two months before harvest (r = 0.62, LCCC = 0.53, MAPE = 27.1%, RMSE = 1.01 t ha−1, and ROC score = 0.88). In addition, drought events throughout the growing season were identified as the main factor causing yield losses in the wheat belt during the past decade. With the increasing availability of farming-related data, we expect that the yield forecasting system proposed in our study may be widely extended to other comparable cropping regions to produce sufficiently accurate wheat yield forecasts for stakeholders to develop strategic decisions in their respective roles.
Woody plant encroachment in agricultural areas reduces agricultural production and is a recognised land degradation problem of global significance. Invasive native scrub (INS) is woody vegetation ...that invades southern Australian rangelands and is commonly cleared to return land to agricultural production. Clearing of INS emits carbon to the atmosphere, and the retention of INS by landholders for the purpose of avoiding carbon emissions has been incentivized in Australia as an emission reduction strategy. Retaining INS, however, means land remains relatively unproductive because INS negatively impacts livestock production. This desktop study examined whether clearing INS to return an area to production, and pyrolysing residues to produce biochar, has the potential to provide climate change mitigation (the “pyrolysis scenario”). The syngas produced via pyrolysis was assumed to be used to generate electricity that was fed into the electricity grid and avoided the production of electricity from existing sources. In addition, the biochar was assumed to be applied to soils used for wheat production, giving mitigation benefits from reduced N2O emissions from fertiliser use and reduction in the use of lime to ameliorate soil acidity. Relative to clearing INS and burning residues in-situ, the pyrolysis scenario resulted in a reduction in radiative forcing of 1.28 × 10−4 W m2 ha−1 of INS managed, 25 years after clearing, and was greater than the reduction of 1.06 × 10−4 W m2 ha−1 that occurred when INS was retained. The greatest contribution to the climate change mitigation provided by the pyrolysis scenario came from avoided emissions from grid electricity production, while avoided N2O and lime emissions made a relatively minor contribution towards mitigation.
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•Encroachment of woody vegetation reduces rangeland productivity in southern Australia.•Current management is to clear scrub and burn residues, emitting C to the atmosphere.•Instead, pyrolysis of residues can stabilise C in biochar and yield renewable energy.•The practice can recover productivity at a C-footprint lower than on-site burning.•Estimate of climate change mitigation potential is larger than retaining live scrub.
•What regulates insect herbivore populations in agroecosystems remain understudied.•A common insect avoids woody vegetation remnants by at least 20 m.•No measured top-down or bottom-up control ...explained the woody vegetation aversion.•Insect density is correlated with 20 % bareground cover and low grass protein content.
Agriculture is a major factor in landscape fragmentation, altering nutrient cycling and animal and plant populations through increasing habitat edge density. Most research on insect herbivores in agroecosystems has focused on the top-down effects of predators moving throughout the habitat mosaic. Few studies have focused on the top-down and bottom-up factors modulating the distribution of insect herbivore populations between natural and agricultural patches. For example, despite an understanding that Australian plague locusts (Chortoicetes terminifera) avoid tree patches, the underlying mechanisms remain unknown. Here, we explored how wooded remnants within pastures affect locust density and the potential top-down and bottom-up mechanistic explanations. We tested three hypotheses: 1) grasses near wooded areas are nutritionally suboptimal, 2) predator density is higher near wooded areas, and 3) temperatures are cooler underneath trees. We measured locust density, grass nutrient content, predator abundance, temperature, and ground cover along 50 m transects from wooded areas to open grassy areas. We ran those transects in three fields and had four transects per field. We confirmed locust avoidance for trees at a 20 m periphery, however none of the variables tested independently explained this trend. Grass nutrient content was similar underneath wooded areas and in open patches. Predator abundance did not differ between the two habitats. The ground was warmer under wooded areas than in grassy areas potentially due to woody vegetation negating windchill. Further, we found that locust density was negatively correlated with plant protein content and was highest in areas with approximately 20 % bare ground cover. Both plant protein and ground cover are important for grasshopper performance and reproduction. It is likely a complex interaction between these variables and others that drive the distribution of this species and other insect herbivores in agroecosystems. The small-scale mechanisms driving the response of insect herbivores to landscape changes is critical to understanding and predicting population dynamics at large-scales.
Reforestation is identified as one of the key nature-based solutions to deliver carbon dioxide removal, which will be required to achieve the net zero ambition of the Paris Agreement. However, the ...potential for sequestration through reforestation is uncertain because climate change is expected to affect the drivers of forest growth. This study used the process-based 3-PG model to investigate the effects of climate change on development of above-ground biomass (AGB), as an indicator of forest growth, in regenerating native forests in southeast Australia. We investigated how changing climate affects AGB, by combining historical data and future climate projections based on 25 global climate models (GCMs) for the Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways. We found that the ensemble means of 25 GCMs indicated an increase in temperature with large variations in projected rainfall. When these changes were applied in 3-PG, we found an increase in the simulated AGB by as much as 25% under a moderate emission scenario. This estimate rose to 51% under a high emission scenario by the end of the 21st century across nine selected sites in southeast Australia. However, when CO2 response was excluded, we found a large decrease in AGB at the nine sites. Our modelling results showed that the modelled response to elevated atmospheric CO2 (the CO2 fertilization effect) was largely responsible for the simulated increase of AGB (%). We found that the estimates of future changes in the AGB were subject to uncertainties originating from climate projections, future emission scenarios, and the assumed response to CO2 fertilization. Such modelling simulation improves understanding of possible climate change impacts on forest growth and the inherent uncertainties in estimating mitigation potential through reforestation, with implications for climate policy in Australia.
•The Physiological Principles in Predicting Growth model was calibrated.•Simulated aboveground biomass increased under future climate scenarios.•Increased carbon dioxide was the major driver for aboveground biomass increase.•Further work is needed to refine the simulated carbon dioxide response in the model.
Animal populations must be able to acquire an adequate amount of nutrients to persist regardless of what environment they are in. In highly variable environments, such as drylands where food sources ...are limited, this potential mismatch between physiological demands and what is available in the environment is accentuated. For herbivores, the balance of macronutrients (protein and carbohydrate) is particularly important and both nutrients are highly variable in plants both spatially and temporally. Whereas it is known that many herbivores will forage multiple plants to achieve an optimal nutritional ratio (termed the intake target), it is less known how herbivores with different life history strategies address this in variable environments. In this study, we measured the intake targets of three grasshopper species with differing life history strategies, two migratory and one non‐migratory, at three locations in New South Wales, Australia. We measured nutrient variation in plants spatially and temporally by sampling three different locations and repeated the measurement twice for one of these locations. At all three locations and both times, host plant protein differed substantially but carbohydrate content remained constant. The non‐migratory grasshopper species shifted their intake target, presumably to redress nutrient imbalances. On the other hand, the two migratory grasshopper species largely maintained the same intake target, even when in a nutritionally suboptimal environment. These results suggest that non‐migratory species are likely more limited in their capacity to forage for optimal diets and may rely more on digestion to survive in nutritionally suboptimal locations. In contrast, migratory grasshoppers may migrate to obtain the nutrients they need instead of redressing imbalances locally. Therefore, a strong metapopulation structure may aid in the persistence of migratory species at larger spatial scales. Since herbivores, especially insects, are important from nutrient cycling to food chains, understanding how populations persist in nutritionally variable environments is important to the overall ecosystem functioning. Further research should consider how nutritional demands drive population dynamics and how it changes with life history strategies.
Grassland production systems contribute 40% to Australia’s gross agricultural production value and utilise >50% of its land area. Across this area, diverse systems exist, but these can be broadly ...classified into four main production systems: (i) pastoral grazing, mainly of cattle at low intensity (i.e. <0.4 dry sheep equivalents/ha) on relatively unimproved native rangelands in the arid and semi-arid regions of northern and central Australia; (ii) crop–livestock systems in the semi-arid zone where livestock graze a mixture of pastures and crops that are often integrated; (iii) high-rainfall, permanent pasture zone in the coastal hinterland and highlands; and (iv) dairy systems covering a broad range of environments and production intensities. A notable trend across these systems has been the decline in sheep numbers and the proportion of income from wool, with beef cattle or sheep meat increasingly important. Although there is evidence that most of these systems have lifted production efficiencies over the past 30 years, total factor productivity growth (i.e. change in output relative to inputs) has failed to match the decline in terms of trade. This has renewed attention on how research and development can help to increase productivity. These industries also face increasing scrutiny to improve their environmental performance and develop sustainable production practices. In order to improve the efficiency and productivity of grassland production systems, we propose and explore in detail a range of practices and innovations that will move systems to new or improved states of productivity or alter efficiency frontiers. These include: filling gaps in the array of pastures available, either through exploring new species or improving the adaptation and agronomic characteristics of species currently sown; overcoming existing and emerging constraints to pasture productivity; improving livestock forage-feed systems; and more precise and lower cost management of grasslands. There is significant scope to capture value from the ecological services that grasslands provide and mitigate greenhouse gas emissions from livestock production. However, large reductions in pasture research scientist numbers (75–95%) over the past 30 years, along with funding limitations, will challenge our ability to realise these potential opportunities.
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.
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.
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
) 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.
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
condition could mitigate the contradiction between production and water consumption and ensure food safety in the NCP.
Linked climate and crop simulation models are widely used to assess the impact of climate change on agriculture. However, it is unclear how ensemble configurations (model composition and size) ...influence crop yield projections and uncertainty. Here, we investigate the influences of ensemble configurations on crop yield projections and modeling uncertainty from Global Gridded Crop Models and Global Climate Models under future climate change. We performed a cluster analysis to identify distinct groups of ensemble members based on their projected outcomes, revealing unique patterns in crop yield projections and corresponding uncertainty levels, particularly for wheat and soybean. Furthermore, our findings suggest that approximately six Global Gridded Crop Models and 10 Global Climate Models are sufficient to capture modeling uncertainty, while a cluster-based selection of 3-4 Global Gridded Crop Models effectively represents the full ensemble. The contribution of individual Global Gridded Crop Models to overall uncertainty varies depending on region and crop type, emphasizing the importance of considering the impact of specific models when selecting models for local-scale applications. Our results emphasize the importance of model composition and ensemble size in identifying the primary sources of uncertainty in crop yield projections, offering valuable guidance for optimizing ensemble configurations in climate-crop modeling studies tailored to specific applications.
•A hybrid model was developed by integrating the APSIM model and the RF model•The hybrid model outperformed the APSIM model in predicting observed wheat yield•The APSIM model might underestimate ...future yield losses caused by climate extremes•Increasing heat events were identified to be the major factor causing future yield losses
Accurately assessing the impacts of extreme climate events (ECEs) on crop yield can help develop effective agronomic practices to deal with climate change impacts. Process-based crop models are useful tools to evaluate climate change impacts on crop productivity but are usually limited in modelling the effects of ECEs due to over-simplification or vague description of certain process and uncertainties in parameterization. In this study, we firstly developed a hybrid model by incorporating the APSIM model outputs and growth stage-specific ECEs indicators (i.e. frost, drought and heat stress) into the Random Forest (RF) model, with the multiple linear regression (MLR) model as a benchmark. The results showed that the APSIM + RF hybrid model could explain 81% of the observed yield variations in the New South Wales wheat belt of south-eastern Australia, which had a 33% improvement in modelling accuracy compared to the APSIM model alone and 19% improvement compared to the APSIM + MLR hybrid model. Drought events during the grain-filling and vegetative stages and heat events immediately prior to anthesis were identified as the three most serious ECEs causing yield losses. We then compared the APSIM + RF hybrid model with the APSIM model to estimate the effects of future climate change on wheat yield. It was interesting to find that future yield projected from single APSIM model might have a 1–10% overestimation compared to the APSIM + RF hybrid model. The APSIM + RF hybrid model indicated that we were underestimating the effects of climate change and future yield might be lower than predicted using single APSIM informed modelling due to lack of adequately accounting for ECEs-induced yield losses. Increasing heat events around anthesis and grain-filling periods were identified to be major factors causing yield losses in the future. Therefore, we conclude that including the effects of ECEs on crop yield is necessary to accurately assess climate change impacts. We expect our proposed hybrid-modelling approach can be applied to other regions and crops and offer new insights of the effects of ECEs on crop yield.
Efficient and effective modelling methods to assess soil organic carbon (SOC) stock are central in understanding the global carbon cycle and informing related land management decisions. However, ...mapping SOC stocks in semi-arid rangelands is challenging due to the lack of data and poor spatial coverage. The use of remote sensing data to provide an indirect measurement of SOC to inform digital soil mapping has the potential to provide more reliable and cost-effective estimates of SOC compared with field-based, direct measurement. Despite this potential, the role of remote sensing data in improving the knowledge of soil information in semi-arid rangelands has not been fully explored. This study firstly investigated the use of high spatial resolution satellite data (seasonal fractional cover data; SFC) together with elevation, lithology, climatic data and observed soil data to map the spatial distribution of SOC at two soil depths (0–5cm and 0–30cm) in semi-arid rangelands of eastern Australia. Overall, model performance statistics showed that random forest (RF) and boosted regression trees (BRT) models performed better than support vector machine (SVM). The models obtained moderate results with R2 of 0.32 for SOC stock at 0–5cm and 0.44 at 0–30cm, RMSE of 3.51MgCha−1 at 0–5cm and 9.16MgCha−1 at 0–30cm without considering SFC covariates. In contrast, by including SFC, the model accuracy for predicting SOC stock improved by 7.4–12.7% at 0–5cm, and by 2.8–5.9% at 0–30cm, highlighting the importance of including SFC to enhance the performance of the three modelling techniques. Furthermore, our models produced a more accurate and higher resolution digital SOC stock map compared with other available mapping products for the region. The data and high-resolution maps from this study can be used for future soil carbon assessment and monitoring.
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•Remote sensing covariates improved the estimation of SOC stocks.•Prediction accuracy of tree-based models was superior to support vector machine.•Digital soil mapping for SOC was practical and cost-effective in semi-arid rangelands.•Fractional cover data influenced SOC stock at the soil surface.
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