• Energy Research

Heterogeneous insolation distribution in agrivoltaic systems (AVS) impacts plant growth beneath solar panels via shading and perturbed evapotranspiration profiles. Most agricultural systems models, meanwhile, assume uniform irradiance patterns across an entire field when simulating biomass production, meaning that they cannot readily account for spatiotemporal trade-offs between agricultural production and energy generation pertaining to AVS. We develop a simple approach for enumerating trade-offs between crop/pasture production and energy generation that accounts for spatial heterogeneity in insolation that typifies most AVS fields. First, long-term spatially explicit daily insolation profiles at the ground surface are produced for several layouts, including variations in PV panel orientations, row spacings, heights and tilt angles. A clustering technique was then applied to all insolation profiles to group them into rationally bounded cluster groups. The insolation profile of each cluster group was set as an input to a conventional point-based agricultural systems model to determine agricultural production under heterogeneous insolation profiles. The proposed approach is applied to a case study near Hobart, Australia, to determine an optimal layout that maximizes energy generation and plant growth associated with 81 AVS layouts. We find a manageable number (19 clusters) of point-based agricultural model scenarios capture much of the variance in insolation variability associated with varying AVS layouts. Compared with open fields, we show that AVS can amplify pasture growth rates during late spring and early summer. The optimal layout for our case study region enhanced land productivity by 47% while maintaining 80% of agricultural production compared with open-field agriculture.

Well-designed agricultural decision support tools (DS) equip farmers with a rapid, easy way to compare multiple scenarios as well as the influence of different management strategies on crop production. One such tool, CropARM (http://www.armonline.com.au) assists users in establishing a framework of risk, with simulations incorporating climate scenarios and management actions, such as fertiliser rates, sowing time, row spacing, and irrigation regimes. When used in conjunction with soil and climate characteristics, biophysical model-based DS tools provide information that complements farmer experience and helps establish a framework for risk management given local climate characteristics. In this study, we used the APSIM model to provide the simulation data necessary to expand CropARM for new management conditions and environments in southern Australia. Prior to this work being undertaken, no CropARM data was available for Tasmania and no sites in CropARM allowed users to compare rainfed and irrigated wheat crops. This study collated data from 27 plots across ten sites in Tasmania, from the period 1981 to 2011, under both rainfed and irrigated conditions. APSIM was parameterised with these field observations and the subsequent scenario simulations were used to populate CropARM. Wheat cultivars used in the parameterisation of APSIM include Brennan, Isis, Mackeller, Revenue, Tennant (winter types) and Kellalac (spring type). The validation showed reliable model parameterisation, with an r2 value of close to 1, which is considered satisfactory. 670,680 simulations were undertaken and incorporated within the CropARM database for wheat cropping systems across Tasmania. With regularly updated climate streams, the free online framework provided by CropARM gives users the ability to assess downside risks associated with several different crop management alternatives, and by simultaneously comparing multiple scenarios, users can select management options that are likely to adhere most closely with their desired management objectives.

Transient and chronic waterlogging constrains crop production in many regions of the world. Here, we invoke a novel iTRAQ-based proteomic strategy to elicit protein synthesis and regulation responses to waterlogging in tolerant (XM 55) and sensitive genotypes (YM 158). Of the 7,710 proteins identified, 16 were distinct between the two genotypes under waterlogging, partially defining a proteomic basis for waterlogging tolerance (and sensitivity). We found that 11 proteins were up-regulated and 5 proteins were down-regulated; the former included an Fe-S cluster assembly factor, heat shock cognate 70, GTP-binding protein SAR1A-like and CBS domain-containing protein. Down-regulated proteins contained photosystem II reaction center protein H, carotenoid 9, 10 (9′, 10′)-cleavage dioxygenase-like, psbP-like protein 1 and mitochondrial ATPase inhibitor. We showed that nine proteins responded to waterlogging with non-cultivar specificity: these included 3-isopropylmalate dehydratase large subunit, solanesyl-diphosphate synthase 2, DEAD-box ATP-dependent RNA helicase 3, and 3 predicted or uncharacterized proteins. Sixteen of the 28 selected proteins showed consistent expression patterns between mRNA and protein levels. We conclude that waterlogging stress may redirect protein synthesis, reduce chlorophyll synthesis and enzyme abundance involved in photorespiration, thus influencing synthesis of other metabolic enzymes. Collectively, these factors accelerate the accumulation of harmful metabolites in leaves in waterlogging-susceptible genotypes. The differentially expressed proteins enumerated here could be used as biological markers for enhancing waterlogging tolerance as part of future crop breeding programs.

Robust quantification of vegetative biomass using satellite imagery using one or more forms of machine learning (ML) has hitherto been hindered by the extent and quality of training data. Here, we showcase how ML predictive demonstrably improves when additional training data is used. We collated field datasets of pasture biomass obtained via destructive sampling, 'C-Dax' reflective measurements and rising plate meters (RPM) from ten livestock farms across four States in Australia. Remotely sensed data from the Sentinel-2 constellation was used to retrieve aboveground biomass using a novel machine learning paradigm hereafter termed "SPECTRA-FOR" (Spectral Pasture Estimation using Combined Techniques of Random-forest Algorithm for Features Optimisation and Retrieval). Using this framework, we show that the low temporal resolution of Sentinel-2 in high latitude regions with persistent cloud cover leads to extensive gaps between cloud-free images, hindering model performance and, thus, contemporaneous ability to forecast real-time pasture biomass. By leveraging the spectral consistency between Sentinel-2 and Planet Lab SuperDove to overcome this limitation, we used ten spectral bands of Sentinel-2, four bands of Sentinel-2 as a proxy for pre-2022 SuperDove (referred to as synthetic SuperDove or SSD), and the actual SuperDove (ASD), given that SuperDove imagery has a higher resolution and more frequent passage compared with Sentinel-2. Using their respective bands as input features to SPECRA-FOR, model performance for the ten bands of Sentinel-2 were R2 = 0.87, root mean squared error (RMSE) of 439 kg DM/ha and mean absolute error (MAE) of 255 kg DM/ha, while that for SSD increased to an R2 of 0.92, RMSE of 346 kg DM/ha and MAE = 208 kg DM/ha. The study revealed the importance of robust data mining, imagery harmonisation and model validation for accurate real-time modelling of pasture biomass with ML.

In 2023, the Australian Government issued ∼250,000 soil carbon credits following a measurement period characterised by high rainfall (Decile 10). The inferred soil organic carbon (SOC) sequestration rates during this period, ranging from ∼2 to 8 t C ha−¹ yr−¹, significantly exceed rates reported in Australian scientific studies (∼0.1 to 1.2 t C ha−¹ yr−¹). Our analysis, incorporating SOC and biomass measurements alongside remote sensing of NDVI, reveals that these SOC gains were largely attributable to above-average rainfall rather than project interventions. Moreover, these gains were not sustained when rainfall returned to average levels, raising concerns about the durability of credited sequestration and its additionality beyond natural climatic variability. Our findings demonstrate that current safeguards within the Soil Carbon Method—such as withholding 25% of credits during the first measurement period—are likely insufficient to account for climatic variability. To strengthen the integrity of the carbon crediting system, we recommend extending the minimum measurement period for credit issuance to at least five years. Additionally, governments should establish science-based ‘reasonable bounds’ for expected long-term SOC gains from management practices to sense-check reported outcomes. These measures will ensure that credited SOC sequestration is more closely tied to management-driven outcomes rather than short-term climate-driven fluctuations. A conceptual diagram of “new” carbon entering the soil system over a 25-year crediting period. Transient fluxes of SOC (blue) versus the accumulation of more persistent SOC (green). The risk of over crediting transient fluctuations of SOC is represented by the circle with a cross.

AbstractModels are key tools in our quest to better understand the impacts of soil waterlogging on plant growth and crop production. Here, we reviewed the state of the art of modeling approaches and compared the conceptual design of these models with recent experimental findings. We show that many models adopt an aeration stress (AS) principle where surplus water reduces air‐filled porosity, with implications for root growth. However, subsequent effects of AS within each model vary considerably. In some cases, AS inhibits biomass accumulation (e.g. AquaCrop), altering processes prior to biomass accumulation such as light interception (e.g. APSIM), or photosynthesis and carbohydrate accumulation (e.g. SWAGMAN Destiny). While many models account for stage‐dependent waterlogging effects, few models account for experimentally observed delays in phenology caused by waterlogging. A model intercomparison specifically designed for long‐term waterlogged conditions (APSIM‐Oryza) with models developed for dryland conditions with transient waterlogging would advance our understanding of the current fitness for purpose of exsiting frameworks for simulating transient waterlogging in dryland cropping systems. Of the point‐based dynamic models examined here, APSIM‐Soybean and APSIM‐Oryza simulations most closely matched with the observed data, while GLAM‐WOFOST achieved the highest performance of the spatial‐regional models examined. We conclude that future models should incorporate waterlogging effects on genetic tolerance parameters such as (1) phenology of stress onset, (2) aerenchyma, (3) root hydraulic conductance, (4) nutrient‐use efficiency, and (5) plant ion (e.g. Fe/Mn) tolerance. Incorporating these traits/effects into models, together with a more systematic model intercomparison using consistent initialization data, will significantly improve our understanding of the relative importance of such factors in a systems context, including feedbacks between biological factors, emergent properties, and sensitive variables responsible for yield losses under waterlogging.

AbstractGlobal climate change is predicted to increase temperatures, alter geographical patterns of rainfall and increase the frequency of extreme climatic events. Such changes are likely to alter the timing and magnitude of drought stresses experienced by crops. This study used new developments in the classification of crop water stress to first characterize the typology and frequency of drought‐stress patterns experienced by European maize crops and their associated distributions of grain yield, and second determine the influence of the breeding traits anthesis‐silking synchrony, maturity and kernel number on yield in different drought‐stress scenarios, under current and future climates. Under historical conditions, a low‐stress scenario occurred most frequently (ca. 40%), and three other stress types exposing crops to late‐season stresses each occurred in ca. 20% of cases. A key revelation shown was that the four patterns will also be the most dominant stress patterns under 2050 conditions. Future frequencies of low drought stress were reduced by ca. 15%, and those of severe water deficit during grain filling increased from 18% to 25%. Despite this, effects of elevated CO2 on crop growth moderated detrimental effects of climate change on yield. Increasing anthesis‐silking synchrony had the greatest effect on yield in low drought‐stress seasonal patterns, whereas earlier maturity had the greatest effect in crops exposed to severe early‐terminal drought stress. Segregating drought‐stress patterns into key groups allowed greater insight into the effects of trait perturbation on crop yield under different weather conditions. We demonstrate that for crops exposed to the same drought‐stress pattern, trait perturbation under current climates will have a similar impact on yield as that expected in future, even though the frequencies of severe drought stress will increase in future. These results have important ramifications for breeding of maize and have implications for studies examining genetic and physiological crop responses to environmental stresses.