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Changes in frequency and severity of heat waves due to climate change pose a considerable challenge to livestock production systems. Although it is well known that heat stress reduces feed intake in cattle, effects of heat stress vary between animal genotypes and climatic conditions and are context specific. To derive a generic global prediction that accounts for the effects of heat stress across genotypes, management and environments, we conducted a systematic literature review and a meta-analysis to assess the relationship between dry matter intake (DMI) and the temperature-humidity index (THI), two reliable variables for the measurement of feed intake and heat stress in cattle, respectively. We analysed this relationship accounting for covariation in countries, breeds, lactation stage and parity, as well as the efficacy of various physical cooling interventions. Our findings show a significant negative correlation (r = − 0.82) between THI and DMI, with DMI reduced by 0.45 kg/day for every unit increase in THI. Although differences in the DMI-THI relationship between lactating and non-lactating cows were not significant, effects of THI on DMI varied between lactation stages. Physical cooling interventions (e.g. provision of animal shade or shelter) significantly alleviated heat stress and became increasingly important after THI 68, suggesting that this THI value could be viewed as a threshold for which cooling should be provided. Passive cooling (shading) was more effective at alleviating heat stress compared with active cooling interventions (sprinklers). Our results provide a high-level global equation for THI-DMI across studies, allowing next-users to predict effects of heat stress across environments and animal genotypes.

Extreme weather events threaten food security, yet global assessments of crop waterlogging are rare. Here, we make three important contributions to the literature. First, we develop a paradigm that distils common stress patterns across environments, genotypes and climate horizons. Second, we embed improved process-based understanding into a contemporary farming systems model to discern changes in global crop waterlogging under future climates. Third, we elicit viable systems adaptations to waterlogging. Using projections from 27 global circulation models, we show that yield penalties caused by waterlogging increased from 3–11% historically to 10–20% by 2080. Altering sowing time and adopting waterlogging tolerant genotypes reduced yield penalties by up to 18%, while earlier sowing of winter genotypes alleviated waterlogging risk by 8%. We show that future stress patterns caused by waterlogging are likely to be similar to those occurring historically, suggesting that adaptations for future climates could be successfully designed using current stress patterns.

Cropping of rice and wheat (Triticum aestivum L.) in rotation contiguously in the same field is a fundamental pillar of double-cropping systems in southern China. Yields of such cropping systems are increasingly challenged as climate change (CC) drives increases in autumnal rainfall, delaying rice harvesting and subsequent sowing of wheat. Here, our purpose was to identify prospective traits of wheat crops enabling adaptation to later sowing and successively truncated growing seasons caused by CC. To identify traits that maintained or improved yields, we constructed 4,096 hypothetical genotypes underpinned by step-wise variations in parameters regulating phenology, growth and yield components. We then assimilated biophysical response surfaces through genotype (G) by environment (E) by management (M) analyses (G×E×M) using six locations spread across the breadth of southern China. We showed that later sowing reduced cumulative radiation interception, cumulative thermal time and crop capture of growing season rainfall. The culmination of these factors shortened crop duration and decreased biomass accumulation and retranslocation after anthesis, reducing grain number and penalising yields. Genotypes that had greater radiation use efficiency, longer juvenile phases and greater grain filling rates were more effective in alleviating yield losses with delayed sowing. However, not even the highest yielding genotype × management combination could entirely alleviate yield losses with delayed sowing. Our results suggest that CC and increasingly frequent extreme climatic events may reduce wheat yields in such cropping systems in the absence of other adaptation.

Salt stress is an ever‐present threat to rice production worldwide. Rice salinity tolerance is complex, both genetically and physiologically. The success and effectiveness in selecting salt‐tolerant rice variety require the identification of QTL for the tolerance and closely linked molecular markers. In the present study, a RIL population consisting of 148 lines, derived from a cross between IR29 (salt‐sensitive) and Pokkali (salt‐tolerant), was used to identify new QTL for salt tolerance and investigate the relationships between salt stress caused injury and the changes in different physiological and morphological traits at the seedling stage. 14,470 high‐quality SNP markers generated by the Rice 56K SNP array were converted to 1,467 bin markers for linkage mapping. A high‐density genetic linkage map covering 1,680.9 cM was constructed, with the physical to genetic distance ratio being 222 Kb/cM. In total, 23 QTL for different salt tolerance indices were identified, including the previously reported Saltol which is currently used in breeding programmes. Three QTL for salt injury score (SIS) were located on chromosomes 1, 4 and 12, all being closely related to the long‐distant Na+ transport from roots to shoots. These QTL showed additive effects, thus can be effectively used in breeding programme to pyramid various tolerance genes.

Barley yellow dwarf (BYD) is a major virus disease which dramatically reduces wheat yield. Introducing BYD resistance genes into commercial varieties has been proven to be effective in reducing damage caused by barley yellow dwarf virus (BYDV). However, only one major resistance gene is readily deployable for breeding; Bdv2 derived from Thinopyrum intermedium is deployed as a chromosomal translocation. In this study, a double haploid (DH) population was developed from a cross between XuBYDV (introduced from China showing very good resistance to BYD) and H-120 (a BYD-sensitive Chinese accession), and was used to identify QTL for BYD resistance. The population was genotyped using an Infinium iSelect bead chip array targeting 90K gene-based SNPs. The disease resistance of DH lines inoculated with BYDV was assessed at the heading stage. The infections were assessed by tissue blot immunoassay (TBIA). Three new QTL were identified on chromosomes 5A, 6A, and 7A for both symptom and TBIA, with all three resistance alleles being inherited from XuBYDV. Some DH lines with the resistance alleles from all three QTL showed high level resistance to BYD. These new QTL will be useful in breeding programs for pyramiding BYD resistance genes.

The regional effects of local water storage are largely unknown. This study identifies, categorizes and discusses the challenges in assessing the potential of local water storage. These are illustrated using a structured method applied to a Dutch case. We conclude that the focus must shift from storage ‘potential’ (the quantity of water that can be stored) to storage ‘feasibility’, which depends on exploitability, purpose and interactions between storage alternatives. Spatial and temporal scale also influence feasibility. Finally, farmers’ investment preferences are a factor, though these are shrouded in uncertainty. This overview is a first step towards improving storage assessment tools and processes.

Here we modelled the influence of phenology of barley crops under diverse environmental and management conditions. Such trait manipulation can assist breeders in genotype selection and growers in better managing barley crops to achieve their yield potential. We first developed two near isogenic lines (NILs) of barley (Eps-317-1-E, and Eps-317-1-L). NILs were developed from a cross between TX9425, a Chinese landrace, and Franklin, an Australian malting barley. Field experiments were then conducted in Tasmania, Australia, using three sowing dates per year during 2015, 2016 and 2017 to parameterise and test the barley module of the APSIM model (APSIM-Barley). We then conducted a genotype by environment by management (GxExM) analysis using ten sites across the Australian wheat-belt, with a range of sowing dates, fertiliser rates and planting densities. The early genotype (Eps-317-1-E) performed better in environments prone to terminal drought and heat stress effects. This was due to earlier flowering and a propensity for greater transpiration-use efficiency from growth stage (GS) 50 to 87. The late NIL (Eps-317-1-L) generally produced higher yield in long-season environments with high rainfall and cool terminal temperatures. Performance of all genotypes was generally better for May sowings (being mid-autumn in the southern hemisphere), wherein yields of the two NILs were highest. Overall, our study showed that Eps-317-1-E was more adapted to regions prone to drought and heat stress, while Eps-317-1-L was more suited to regions with longer growing seasons. This study exemplifies how models can be used in concert with breeding experiments and thus provides farmers and breeders with opportunities to examine how new genotypes will perform in diverse environments under multiple management conditions.

Food supply chains are the backbone of our global food systems. Ensuring that these supply chains are robust and can withstand disruptions such as climate change and pandemics is vital for all societies. Just as important is the supply chains’ sustainability, given that they are a major emitter of greenhouse gasses (GHG). To meet this dual challenge requires new, interdisciplinary strategies that draw on scientific, technical and social innovations right across the disciplinary spectrum. Addressing this challenge was the main motivation for this special issue.

Foresight and trade-off analyses offer organizations such as CGIAR an opportunity to better prepare for alternative futures through adaptive research strategy and management. This essay introduces a set of papers that explore foresight and trade-off analyses within the context of the major reforms now occurring in the CGIAR. We tease out lessons not only for One CGIAR, but also for international development research organizations more broadly.

This review evaluates the sustainability of tomato production in four greenhouse systems: high-tech (The Netherlands) and low-tech (Spain) combined with two ways of cultivation (conventional or organic). The Sustainable Development Goals (SDGs), as defined by the United Nations, were used as a lens to assess the sustainability of these four greenhouse production systems. In total seven SDGs, including 14 targets, were assessed through 12 quantitative and two descriptive indicators. Conventional, high-tech greenhouse systems showed the greatest potential for positive contributions towards four of the SDGs. However, their relatively high energy use makes it difficult to achieve SDG7 on affordable and clean energy, where low-tech systems perform better due to lower energy use from relatively cleaner sources. Lower water use efficiency and higher nutrient losses in all soil-based cultivation systems are barriers to achieving some targets under most of the selected SDGs. Organic cultivation systems showed relatively high water and land use, based on the limited data available. Our review highlights the existence of substantial synergies, but also considerable trade-offs between SDGs. This needs to be considered when making policy, investment and management decisions related to greenhouse production.

Models 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.

In‐crop soil waterlogging can be caused by extreme rainfall events, high ground water tables, excessive irrigation, lateral ground water flow, either individually or in concert, and together these factors inhibit potential grain yields. However, the extent to which yield is influenced by the timing and duration of waterlogging relative to crop phenology is unknown. To investigate this, we conducted a range of waterlogging treatments on modern barley genotypes differing in their waterlogging tolerance, with tolerance conferred through aerenchyma formation under oxygen deficit conditions. Experiment 1 was conducted in a controlled environment using four waterlogging treatments: waterlogging at Zadoks stage (ZS) 12.5 for 1 or 2 months (WL1 and WL2, respectively), waterlogging at ZS 15 for 2 months (WL3), and waterlogging initiated 1 day before heading for 15 days (WL4). Experiment 2 was conducted in the field with WL2. Averaged across experiments, yield was reduced by 35% in W1 to 52% in WL3 due to fewer spikes/m2 and kernels/spike. WL4 had the greatest impact on yield (70% reduction) due to its effect on spikelet fertility and grain filling. Phenology was delayed 1–8 ZS at the end of waterlogging treatments, with the waterlogging‐susceptible cultivar Franklin showing the greatest delays, and waterlogging tolerant genotypes (Macquarie+, TAMF169) capable of aerenchyma formation under waterlogging having the least delays (0–4 ZS). Genotypes with aerenchyma formation QTL (Macquarie+) showed nonsignificant yield reduction compared with nonwaterlogged controls, preventing 23% yield loss under early phenological waterlogging stress. Late growth stage waterlogging substantially reduced average final grain yield by 70%.

Climate change (CC) in central China will change seasonal patterns of agricultural production through increasingly frequent extreme climatic events (ECEs). Breeding climate-resilient wheat (Triticum aestivum L.) genotypes may mitigate adverse effects of ECEs on crop productivity. To reveal crop traits conducive to long-term yield improvement in the target population of environments, we created 8,192 virtual genotypes with contrasting but realistic ranges of phenology, productivity and waterlogging tolerance. Using these virtual genotypes, we conducted a genotype (G) by environment (E) by management (M) factorial analysis (G×E×M) using locations distributed across the entire cereal cropping zone in mid-China. The G×E×M invoked locally-specific sowing dates under future climates that were premised on shared socioeconomic pathways SSP5-8.5, with a time horizon centred on 2080. Across the simulated adaptation landscape, productivity was primarily driven by yield components and phenology (average grain yield increase of 6-69% across sites with optimal combinations of these traits). When incident solar radiation was not limiting carbon assimilation, ideotypes with higher grain yields were characterised by earlier flowering, higher radiation-use efficiency and larger maximum kernel size. At sites with limited solar radiation, crops required longer growing periods to realise genetic yield potential, although higher radiation-use efficiency and larger maximum kernel size were again prospective traits enabling higher rates of yield gains. By 2080, extreme waterlogging stress in some regions of mid-China will impact substantially on productivity, with yield penalties of up to 1,010 kg ha

A reliable method was developed to screen cereal crops for BYDV‐PAV resistance in glasshouse experiments. This also entailed the evaluation of traits associated with Barley yellow dwarf virus (BYDV) infection such as leaf discolouration, reduction in growth, biomass and yield traits, and percentage of virus‐infected plants, using enzyme‐linked immunosorbent assay (ELISA) and tissue blot immunoassay (TBIA). Four glasshouse experiments were conducted with eight wheat, barley and oat varieties inoculated with BYDV‐PAV at the 2‐leaf stage, using different numbers of viruliferous aphids and different inoculation periods and temperatures. Inoculation with 5–10 viruliferous aphids per plant for 4 days led to a high percentage of infection in susceptible varieties, indicating that this is an effective BYDV screening method when selecting for resistance in cereal crops. For barley and oat, visual evaluation of symptoms is considered adequate for assessing BYDV resistance. However, for wheat it is necessary to evaluate BYDV resistance by ELISA/TBIA tests and plant biomass (at early stage) and grain number and yield (at late stage) measurements.

While seasonal outlooks have been operational for many years, until recently the extended-range timescale referred to as subseasonal-to-seasonal (S2S) has received little attention. S2S prediction fills the gap between short-range weather prediction and long-range seasonal outlooks. Decisions in a range of sectors are made in this extended-range lead time; therefore, there is a strong demand for this new generation of forecasts. International efforts are under way to identify key sources of predictability, improve forecast skill and operationalize aspects of S2S forecasts; however, challenges remain in advancing this new frontier. If S2S predictions are to be used effectively, it is important that, along with science advances, an effort is made to develop, communicate and apply these forecasts appropriately. In this study, the emerging operational S2S forecasts are presented to the wider weather and climate applications community by undertaking the first comprehensive review of sectoral applications of S2S predictions, including public health, disaster preparedness, water management, energy and agriculture. The value of applications-relevant S2S predictions is explored, and the opportunities and challenges facing their uptake are highlighted. It is shown how social sciences can be integrated with S2S development, from communication to decision-making and valuation of forecasts, to enhance the benefits of ‘climate services’ approaches for extended-range forecasting. While S2S forecasting is at a relatively early stage of development, it is concluded that it presents a significant new window of opportunity that can be explored for application-ready capabilities that could allow many sectors the opportunity to systematically plan on a new time horizon.

Context Since COVID-19 (SARS-CoV-2) was first identified in the human population, it has had immediate and significant effects on peoples' health and the worldwide economy. In the absence of a vaccine, control of the virus involved limiting its spread through restrictions in the movement of people, goods and services. This has led to unprecedented impacts on labour availability, provision of goods and services, value chains, and markets. Objective Against the backdrop of COVID-19 control measures, this article summarises quantitative and qualitative assessments of the impacts, adaptations, and opportunities to increase the resilience of the agricultural systems in Australia and New Zealand. Methods Using both survey and interview methodologies, we describe the various agri-food systems and the impacts of the COVID-19 control measures across different industries, and discuss the results applying a resilience framework. Results As essential services, all agricultural activities except for fibre production have been permitted to continue during quarantine periods but have been exposed to the major flow-on effects of movement control. We found that, to June 2020, the impacts of the COVID-19 control measures on the agri-food sectors in both Australia and New Zealand have been relatively small and that this has been due to the high levels of resilience in the agricultural systems and the people running them. Conclusions We consider agri-food systems to be comprised of multiple subsystems with varying vulnerability to external influences. Agri-food systems were resilient to June 2020 at least, and that resilience was achieved via one or more subsystems that were able to compensate for the more vulnerable subsystems. We contrast the resilience of industries that have high plasticity (that can have a flow of material that can safely vary in time) to more rigid industries that are dependent on a steady flow of material with little or no storage. Ultimately both types of industries were resilient, but they achieved that resilience via compensating subsystems. High plasticity industries relied on their production and processing subsystem; rigid industries engaged their institutional subsystem to achieve the same end. The social and cultural subsystem was important across all industries. Significance It is not yet clear if the current resilience mechanisms can persist under the continued onslaught of the virus. We indicate the need to capture longer term effects and analysis during the more sustained effects of the virus and through a recovery period. We anticipate a follow-up study in 2022.

Heading date (HD) is an important agronomic trait that influences plant adaptability to varying environment and, ultimately, grain yield. In this study, two doubled haploid (DH) populations were used to identify new QTL for HD. One of the DH populations is originated from a cross between an Australian malting barley cv. Franklin and a wild barley accession TAM407227 and the other one is from the cross between a Syrian wild barley SYR01 and an Australian malting barley cv. Gairdner. Using three times of sowing (TOS) differing in daylength and temperature, we investigated quantitative trait loci (QTL) controlling HD from both populations. Fourteen QTL were identified for HD from different populations and sowing dates. The expression of HD related genes varied with the TOS, suggesting a significant QTL × environment interaction. By comparing the positions of previously mapped HD genes and those of QTL detected in this population, we found that eleven of the fourteen QTL identified in this study were located at similar positions to those reported genes for HD. Among the three new potential QTL, one was located at 73.5 cM on chromosome 2H, explaining 19.2% and 4.6% HD of DH lines in spring and summer growing, respectively. The wild barley parent TAM407227 contributed the early maturity allele. HORVU2Hr1G088460 within the interval of QTL could be the candidate gene. The second new QTL was identified on chromosome 3H from a summer sowing trial and the third one on chromosome 4H affected HD of DH lines only under spring sowing condition. These new QTL identified will provide alternative genetic resources for plant breeders developing barley varieties with improved HD adaptability to varying environments.

Halophytic Oryza coarctata is a good model system to examine mechanisms of salinity tolerance in rice. O. coarctata leaves show the presence of microhairs in adaxial leaf surface furrows that secrete salt under salinity. However, detailed molecular and physiological studies of O. coarctata microhairs are limited due to their relative inaccessibility. This work presents a detailed characterization of O. coarctata leaf features. O. coarctata has two types of microhairs on the adaxial leaf surface: longer microhairs (three morphotypes) lining epidermal furrow walls and shorter microhairs (reported first time) arising from bulliform cells. Microhair morphotypes include (i) finger-like, tubular structures, (ii) tubular hairs with bilobed and flattened heads and (iii) bi-or trifurcated hairs. The unicellular nature of microhairs was confirmed by propidium iodide (PI) staining. An efficient method for the isolation and enrichment of O. coarctata microhairs is presented (yield averaging ∼2 × 105/g leaf tissue). The robustness of the microhair isolation procedure was confirmed by subsequent viability staining (PI), total RNA isolation and RT-PCR amplification of O. coarctata trichome-specific WUSCHEL-related homeobox 3B (OcWox3B) and transporter gene-specific cDNA sequences. The present microhair isolation work from O. coarctata paves the way for examining genes involved in ion secretion in this halophytic wild rice model.

Crop models were originally developed for application at the field scale but are increasingly used to assess the impact of climate and/or agronomic practices on crop growth and yield and water dynamics at larger scales. This raises the question of how data aggregation approaches affect outputs when using crop models at large spatial scales. This study investigates how input and output data aggregation affected simulated rainfed and irrigated potato yield and irrigation water requirement (IWR) across potato production areas in Tasmania, Australia. First, the yield and IWR with aggregated model inputs at 15, 25 and 40 km resolutions (input aggregation) was simulated. Second, simulated model outputs generated with high-resolution input data were aggregated to 15, 25 and 40 km resolutions (output aggregation) and compared to the corresponding yield and IWR with simulations based on input data aggregation. Finally, the differences (D) (DY and DIWR for yield and IWR, respectively) between grids using input and output aggregation were evaluated. The results indicate that the effect of input and output data aggregation on yield depends on water-driven factors including plant available water capacity (PAWC), rainfall and irrigation. Maximum D values were found for rainfed yield (4.4 t ha-1) and IWR (137 mm). DY variations were correlated with the differences of PAWC caused by data aggregation in 82 % of potato production areas. Differences between aggregation methods were reduced when growing season rainfall increased. We conclude that PAWC and the source of water (rainfall or rainfall + irrigation) explained the larger errors associated with the input and output data aggregation on simulated potato yield and IWR. Future studies should consider the data aggregation method in their assessment to minimize errors and therefore produce higher quality advice or farming decisions.

Groundwater levels in the North China Plain (NCP), the bread basket of China, have dropped more than one meter per year over the last 40 years, putting at risk the long term productivity of this region. Groundwater decline is most severe in the Alluvial Fans where our study site is located. Avoiding a foreseeable systems collapse requires region-wide changes in crop systems management, underpinned by sound environmental policies. Here, we explore the potential of crop system adaptation to remedy the excessive water use and quantify the likely yield penalties associated with more sustainable water use practices. Using simulations with the APSIM cropping systems model we explore production opportunities in an area within the NCP with intensive cropping and no access to irrigation from rivers. We estimate the attainable production levels for wheat and maize if agriculture were made groundwater neutral, through changes in crop sequence, irrigation practices and water conservation technologies (e.g. mulching with plastic film). Total grain production would drop by 44% compared to current practice if agriculture were made groundwater neutral. Water conservation by plastic film could limit this reduction to 21-33% but possible environmental impacts of plastic film need attention. This analysis facilitates a much needed debate on alternative agronomic practices and incentives through a quantitative comparison of adaptation options. Our biophysical analysis needs to be complemented with socio-economic considerations and discussions with all stakeholders. Similar analyses in other parts of the NCP are possible but require more accurate modelling of landscape hydrology and (towards the coast) risk of salt water intrusion.

The images and perceived roles of agriculture in our societies have changed over the last few decades. Today agriculture is regarded as an integral part of interconnected value chains that sit at the heart of our economies, providing invaluable services to society. In response, most governments around the world are now actively developing policies to support and grow their bio-economies. This increases the expectations that society and governments have in terms of agriculture’s services and performance: agriculture is not only expected to generate food for our growing populations and income for farmers, it must be part of value chains that provide raw materials that can be incorporated or converted into feed, fiber, fuel, pharmaceuticals, and other industrial products. Farmers are expected to be responsible custodians of our landscapes and their farming practices must be economically, environmentally, and socially sustainable and aligned with the broader and changing values of our societies. Often these three objectives conflict and consequently societal expectations are not met. In a world that is increasingly data rich, practicing agriculture in a way that lives up to these expectations requires tools that can help to foresee the consequences of complex interactions. Hence, this chapter explores the role of modeling and systems thinking to manage this complexity by explicitly considering three attributes of complex, adaptive systems, whereby (i) order emerges rather than being predetermined; (ii) the system’s future can only be assessed probabilistically rather than deterministically predicted; and (iii) the history of the system is largely irreversible. The chapter reflects on the contemporary use of models against these three systems characteristics and concludes that scientifically based and tested algorithms (i.e., models) are already a ubiquitous and indispensable management tool for modern farming. Countless apps are already in use for short-term, tactical decision making, while more complex modeling approaches are vital for strategic scenario planning and risk assessments for farmers, policymakers, and scientists.

Regional long-term water management plans depend increasingly on investments by local water users such as farmers. However, local circumstances and individual situations vary and investment decisions are made under uncertainty. Water users may therefore perceive the costs and benefits very differently, leading to non-uniform investment decisions. This variation can be explored using crossover points. A crossover point represents conditions in which a decision maker assigns equal preference to competing alternatives. This paper presents, applies, and evaluates a framework extending the use of the concept of crossover points to a participatory process in a group setting. We applied the framework in a case study in the Coal River Valley of Tasmania, Australia. Here, farmers can choose from multiple water sources. In this case, the focus on crossover points encouraged participants to engage in candid discussions exploring the personal lines of reasoning underlying their preferences. Participants learned from others&rsquo, inputs, and group discussions elicited information and insights considered valuable for both the participants and for outsiders on the factors that influence preferences. We conclude that the approach has a high potential to facilitate learning in groups and to support planning.

Frost, drought and heat are key abiotic constraints to barley production in Australia. To maximise grain yield, crop life-cycle needs to be timed to minimise growth stresses associated with insufficient radiation, frost, heat and drought stress during the critical period for yield determination that occurs prior to and during flowering. To identify optimal flowering times for commercial Australian barley genotypes, we conducted a genotype (G) × environment (E) × management (M) analysis using climate data from locations distributed throughout the Australian barley growing regions. Prior to conducting the G×E×M, we parameterised key phenological variables in the APSIM-Barley module for all genotypes using photoperiod and vernalisation data from experimental treatments, then we verified the model using seven independent field experiments. The verification process showed an average RMSE of 1.4–7.2 days and R2 values of 0.83–1.00 depending on genotype, indicating reasonable performance of the model in simulating phenology. Using the parameterised model, we then characterised water, frost and heat stress patterns for a range of sowing date × genotype combinations. The G×E×M showed that optimal flowering period (OFP) was a function of the environment more so than the genotype, and the relative importance of insufficient radiation, frost, heat and water stress varied signficantly with location. OFP was earlier (mid August to late September), in Western Australia and South Australia, while OFP was later (mid-October to mid-November) in Tasmania and Victoria. In low rainfall environments, the duration of the OFP was shorter in most cases than in high rainfall environments. In general, earlier sowing (1 April to 22 April) resulted in lower yield across sites, and this was more pronounced for fast developing genotypes La Trobe, Fathom, Compass and Alestar. Later sowing (1 June to 30 June) at Condobolin (NSW), Hopetoun (WA) and Loxton (SA) also resulted in a lower yield for all genotypes. Knowledge of OFPs based on long-term abiotic stresses will allow breeders to develop genotypes with phenological durations that are pertinent to each location. This will also allow farmers to select sowing dates according to genotype duration and thus minimise the combined risk of frost, heat and water stresses, which collectively should allow yield to approach its maximum potential.

Input data aggregation affects crop model estimates at the regional level. Previous studies have focused on the impact of aggregating climate data used to compute crop yields. However, little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) on irrigation water requirement (IWR) in cool-temperate and spatially heterogeneous environments. The aims of this study were to quantify DAEc and DAEs of model input data and their combined impacts for simulated irrigated and rainfed yield and IWR. The Agricultural Production Systems sIMulator Next Generation model was applied for the period 1998–2017 across areas suitable for potato (Solanum tuberosum L.) in Tasmania, Australia, using data at 5, 15, 25 and 40 km resolution. Spatial variances of inputs and outputs were evaluated by the relative absolute difference ( r AD ¯ ) between the aggregated grids and the 5 km grids. Climate data aggregation resulted in a r AD ¯ of 0.7–12.1%, with high values especially for areas with pronounced differences in elevation. The r AD ¯ of soil data was higher (5.6–26.3%) than r AD ¯ of climate data and was mainly affected by aggregation of organic carbon and maximum plant available water capacity (i.e. the difference between field capacity and wilting point in the effective root zone). For yield estimates, the difference among resolutions (5 km vs. 40 km) was more pronounced for rainfed ( r AD ¯ = 14.5%) than irrigated conditions ( r AD ¯ = 3.0%). The r AD ¯ of IWR was 15.7% when using input data at 40 km resolution. Therefore, reliable simulations of rainfed yield require a higher spatial resolution than simulation of irrigated yields. This needs to be considered when conducting regional modelling studies across Tasmania. This study also highlights the need to separately quantify the impact of input data aggregation on model outputs to inform about data aggregation errors and identify those variables that explain these errors.

The main objective of this study was to assess the impact of biochar rate (0, 8, 16 and 32 Mg ha−1) on the water retention capacity (WRC) of a sandy loam Dystric Plinthosol. The applied biochar was a by-product of slow pyrolysis (∼450 °C) of eucalyptus wood, milled to pass through a 2000 μm sieve that resulted in a material with an intrinsic porosity ≤10 μm and a specific surface area of ∼3.2 m2 g−1. The biochar was incorporated into the top 15 cm of the soil under an aerobic rice system. Our study focused on both the effects on WRC and rice yields 2 and 3 years after its application. Undisturbed soil samples were collected from 16 plots in two soil layers (5–10 and 15–20 cm). Soil water retention curves were modelled using a nonlinear mixed model which appropriately accounts for uncertainties inherent of spatial variability and repeated measurements taken within a specific soil sample. We found an increase in plant-available water in the upper soil layer proportional to the rate of biochar, with about 0.8% for each Mg ha−1 biochar amendment 2 and 3 years after its application. The impact of biochar on soil WRC was most likely related to an effect in overall porosity of the sandy loam soil, which was evident from an increase in saturated soil moisture and macro porosity with 0.5 and 1.6% for each Mg ha−1 of biochar applied, respectively. The increment in soil WRC did not translate into an increase in rice yield, essentially because in both seasons the amount of rainfall during the critical period for rice production exceeded 650 mm. The use of biochar as a soil amendment can be a worthy strategy to guarantee yield stability under short-term water-limited conditions. Our findings raise the importance of assessing the feasibility of very high application rates of biochar and the inclusion of a detailed analysis of its physical and chemical properties as part of future investigations.

Barley yellow dwarf virus-PAV (BYDV-PAV) is one of the major viruses causing a widespread and serious viral disease affecting cereal crops. To gain a better understanding of plant defence mechanisms of BYDV resistance genes ( Bdv2 and Yd2 ) against BYDV-PAV infection, the differences in agronomical, biochemical and histological changes between susceptible and resistant wheat and barley cultivars were investigated. We found that root growth and total dry matter of susceptible cultivars showed greater reduction than that of resistant ones after infection. BYDV infected leaves in susceptible wheat and barley cultivars showed a significant reduction in photosynthetic pigments, an increase in the concentration of reducing sugar. The protein levels were also low in infected leaves. There was a significant increase in total phenol contents in resistant cultivars, which might reflect a protective mechanism of plants against virus infection. In phloem tissue, sieve elements (SE) and companion cells (CC) were severely damaged in susceptible cultivars after infection. It is suggested that restriction of viral movement in the phloem tissue and increased production of phenolic compounds may play a role in the resistance and defensive mechanisms of both Bdv2 and Yd2 against virus infection.

Climate variability and change are risk factors for climate sensitive activities such as agriculture. Managing these risks requires "climate knowledge", i.e. a sound understanding of causes and consequences of climate variability and knowledge of potential management options that are suitable in light of the climatic risks posed. Often such information about prognostic variables (e.g. yield, rainfall, run-off) is provided in probabilistic terms (e.g. via cumulative distribution functions, CDF), whereby the quantitative assessments of these alternative management options is based on such CDFs. Sound statistical approaches are needed in order to assess whether difference between such CDFs are intrinsic features of systems dynamics or chance events (i.e. quantifying evidences against an appropriate null hypothesis). Statistical procedures that rely on such a hypothesis testing framework are referred to as "inferential statistics" in contrast to descriptive statistics (e.g. mean, median, variance of population samples, skill scores). Here we report on the extension of some of the existing inferential techniques that provides more relevant and adequate information for decision making under uncertainty.

Expected increases in food demand and the need to limit the incorporation of new lands into agriculture to curtail emissions, highlight the urgency to bridge productivity gaps, increase farmers profits and manage risks in dryland cropping. A way to bridge those gaps is to identify optimum combination of genetics (G), and agronomic managements (M) i.e. crop designs (GxM), for the prevailing and expected growing environment (E). Our understanding of crop stress physiology indicates that in hindsight, those optimum crop designs should be known, while the main problem is to predict relevant attributes of the E, at the time of sowing, so that optimum GxM combinations could be informed. Here we test our capacity to inform that “hindsight”, by linking a tested crop model (APSIM) with a skillful seasonal climate forecasting system, to answer “What is the value of the skill in seasonal climate forecasting, to inform crop designs?” Results showed that the GCM POAMA-2 was reliable and skillful, and that when linked with APSIM, optimum crop designs could be informed. We conclude that reliable and skillful GCMs that are easily interfaced with crop simulation models, can be used to inform optimum crop designs, increase farmers profits and reduce risks.

Our study suggests the possibility for transformational change in the productivity and risk profile of some of India's rainfed cropping systems. In the semi-arid regions of Southern India, farmers traditionally crop sorghum or chickpea on Vertisols during the post-rainy season, keeping the fields fallow during the rainy season. This practice avoids land management problems, but limits the potential for crop intensification to increase systems productivity. A long-term (15 year) experiment at ICRISAT demonstrated that cropping during the rainy season is technically feasible, and that grain productivity of double cropped sorghum + chickpea (SCP–SCP) and mung bean + sorghum (MS–MS) sequential systems were higher than their conventional counterparts with rainy season fallow, i.e. fallow + post-rainy sorghum (FS–FS) and fallow + post-rainy chickpea (FS–FCP). Without N application, mean grain yield of post-rainy sorghum in the MS–MS system was significantly greater (2520 kg ha−1 per two-year rotation) than in the FS–FS system (1940 kg ha−1 per two-year rotation), with the added benefit of the mung bean grain yield (1000 kg ha−1 per two-year rotation) from the MS–MS system. In the SCP–SCP system the additional grain yield of rainy sorghum (3400 kg ha−1 per two-year rotation) ensured that the total productivity of this system was greater than all other systems. Double cropping MS–MS and SCP–SCP sequential systems had significantly higher crop N uptake compared to traditional fallow systems at all rates of applied nitrogen (N). The intensified MS–MS and SCP–SCP sequential systems without any N fertilizer applied recorded a much higher median gross profit of Rs. 20,600 (US $ 375) and Rs. 15,930 (US $ 290) ha−1 yr−1, respectively, compared to Rs. 1560 (US $ 28) ha−1 yr−1) with the FS–FS system. Applying 120 kg of N ha−1 considerably increased the profitability of all systems, lifting median gross profits of the sorghum + chickpea system over Rs. 60,000 (US $ 1091) ha−1 yr−1 and the conventional system to Rs. 20,570 (US $ 374) ha−1 yr−1. The gross profit margin analysis showed that nitrogen is a key input for improving productivity, particularly for the double cropping systems. However, traditional systems are unviable and risky without N application in the variable climates of the semi-arid tropics. Together, our results show that on Vertisols in semi-arid India, double cropping systems increase systems’ productivity, and are financially more profitability and less risky than traditional fallow post-rainy systems while further benefits can be achieved through fertilizer application.

Barley yellow dwarf virus infection (BYDV) often results in substantial yield losses in susceptible cereal crops. Major symptoms of BYDV infection in cereals include plant dwarfing and colour changes of leaf blades along the vascular bundles, especially of leaf tips. A full understanding of physiological and molecular mechanisms contributing to resistance provides salient information for breeding BYD resistant varieties and developing strategies to address the problem. In this paper, we reviewed BYDV infection mechanisms and summarised current information on known resistance genes, molecular markers and the use of transgenic techniques in breeding of BYD resistant varieties. Cereal yellow dwarf viruses (CYDVs) are also discussed as both BYDV and CYDV belong to the family Luteoviridae.

The objective of this work was to investigate the impact of the application of wood biochar, combined with N fertilizations, on N2O-N fluxes, nitrogen availability, and water-filled pore space (WFPS) of a clayey Oxisol under rice (wet season) and common bean (dry season) succession. Manual static chambers were used to quantify N2O-N fluxes from soil immediately after a single application of wood biochar (32 Mg ha-1) and after four crop seasons with N applications (90 kg ha-1 N). Soil ammonium (N-NH4+) and nitrate (N-NO3-) availability, as well as WFPS, was measured together with N2O-N fluxes. There was no interaction between biochar and N fertilization regarding N2O-N fluxes in any of the four seasons monitored, although these fluxes were clearly enhanced by N applications. At 1.5 and 2.5 years after biochar application, the WFPS decreased. In addition, in the seasons characterized by low WFPS, N2O-N fluxes and soil N-NO3- and N-NH4+ availability were enhanced after N applications. Long-term experiments in the field are important to quantify the impacts of biochar on N2O-N fluxes and to determine the dynamics of these fluxes on soil-related variables. Resumo O objetivo deste trabalho foi investigar o impacto da aplicação de biochar de madeira, combinada com fertilizações nitrogenadas, nos fluxos de N2O-N, na disponibilidade de nitrogênio e no espaço poroso preenchido por água (EPPA), em um Latossolo argiloso sob sucessão com arroz (época chuvosa) e feijão (época seca). Câmaras estáticas manuais foram utilizadas para quantificar os fluxos de N2O-N no solo logo após uma única aplicação de biochar (32 Mg ha-1) e após quatro épocas de cultivo com aplicações de N (90 kg ha-1 de N). A disponibilidade de amônio (N-NH4+) e de nitrato (N-NO3-) no solo, bem como o EPPA, foi medida juntamente com os fluxos de N2O-N. Não houve interação entre biochar e fertilização nitrogenada quanto aos fluxos de N2O-N, em nenhuma das quatro épocas monitoradas, apesar de esses fluxos terem aumentado com as aplicações de N. Aos 1,5 e 2,5 anos após a aplicação do biochar, o EPPA diminuiu. Além disso, nas épocas caracterizadas por reduzido EPPA, os fluxos de N2O-N e a disponibilidade de N-NO3- e N-NH4+ no solo aumentaram após as aplicações de N. Experimentos em campo de longa duração são importantes para quantificar o impacto do uso de biochar sobre os fluxos de N2O-N e para determinar a dinâmica desses fluxos sobre as variáveis relacionadas ao solo.

Decreasing water availability for rice based systems resulted in the introduction of water saving production systems such as aerobic rice and alternate wetting–drying technology. To further improve resource use efficiency in these systems, water management should be attuned to critical growth stages, requiring accurate prediction of crop phenology. Photoperiod-sensitivity of aerobic rice genotypes complicates the estimation of the parameters characterising phenological development and hence impairs predictions. To overcome this complication, we followed a two-step approach: (1) the photoperiod response was determined in growth chambers, through a reciprocal transfer experiment with variable day length, conducted at a fixed temperature, and consecutively, (2) the temperature response was studied by combining the obtained photoperiod parameters with data from field experiments. All four aerobic rice genotypes tested exhibited strong photoperiod-sensitivity. Durations of basic vegetative phase (BVP) i.e. when plants are still insensitive to photoperiod, photoperiod-sensitive phase (PSP), and post-PSP (PPP) varied among genotypes. The temperature response of the genotypes was explored by combining phenological observations in the reciprocal transfer experiment with observations in two field experiments. The temperature range in the field experiments was too narrow to obtain convergence to a unique set of temperature response parameters, regardless whether a bilinear or a beta model was used. Sensitivity analysis however provided clear arguments in support of the recent doubts on the validity of a commonly used set of cardinal temperatures for rice phenology. Using standard cardinal temperatures, the rate of development at temperatures below 31 °C was overestimated. This finding stresses the need for experiments on rice phenology under a wider range of temperatures.

Concepts of agricultural sustainability and possible roles of simulation modelling for characterising sustainability were explored by conducting, and reflecting on, a sustainability assessment of rain-fed wheat-based systems in the Middle East and North Africa region. We designed a goal-oriented, model-based framework using the cropping systems model Agricultural Production Systems sIMulator (APSIM). For the assessment, valid (rather than true or false) sustainability goals and indicators were identified for the target system. System-specific vagueness was depicted in sustainability polygons—a system property derived from highly quantitative data—and denoted using descriptive quantifiers. Diagnostic evaluations of alternative tillage practices demonstrated the utility of the framework to quantify key bio-physical and chemical constraints to sustainability. Here, we argue that sustainability is a vague, emergent system property of often wicked complexity that arises out of more fundamental elements and processes. A ‘wicked concept of sustainability’ acknowledges the breadth of the human experience of sustainability, which cannot be internalised in a model. To achieve socially desirable sustainability goals, our model-based approach can inform reflective evaluation processes that connect with the needs and values of agricultural decision-makers. Hence, it can help to frame meaningful discussions, from which actions might emerge.