Resilience Of Agricultural Systems Research Articles

Reviewing the impact of climate change on global food security: Challenges and solutions

This review examines the intricate relationship between climate change and global food security, elucidating the challenges posed by climate variability and exploring potential solutions to mitigate its adverse effects. Climate change presents formidable obstacles to food security worldwide, disrupting agricultural productivity, exacerbating food shortages, and deepening malnutrition. Shifts in temperature and precipitation patterns, coupled with the increasing frequency of extreme weather events, threaten crop yields, livestock health, and food distribution networks, particularly in vulnerable regions. The review identifies key challenges confronting global food security in the context of climate change, including diminished agricultural productivity, heightened food price volatility, and heightened risks of food insecurity and malnutrition. It underscores the disproportionate impact of climate change on marginalized communities, smallholder farmers, and low-income populations, who bear the brunt of environmental degradation and lack the resources to adapt. In response to these challenges, the review explores a range of solutions and strategies aimed at enhancing food security resilience in a changing climate. It discusses the importance of climate-smart agricultural practices, such as agroforestry, crop diversification, and soil conservation, in bolstering the resilience of agricultural systems and improving resource efficiency. Additionally, the review highlights the significance of enhancing access to climate information, investing in sustainable infrastructure, and promoting gender equity in agriculture to foster inclusive and adaptive food systems. Furthermore, the review underscores the critical role of policy interventions, international collaboration, and community engagement in addressing climate-related food security challenges and advancing sustainable development goals. By integrating climate adaptation and mitigation measures into agricultural policies, promoting research and innovation, and fostering multi-stakeholder partnerships, it is possible to build more resilient and equitable food systems that can withstand the impacts of climate change and ensure food security for all. In conclusion, the review emphasizes the urgency of concerted action to address the complex interplay between climate change and global food security. Through collective efforts and targeted interventions, it is feasible to mitigate the adverse effects of climate change on food systems and pave the way for a more sustainable and food-secure future. Keywords: Impact, Climate Change, Global, Food Security, Challenges.

Unravelling the complexities of wetland agriculture, climate change, and coping mechanisms: an integrative review using economics and satellite approaches

AbstractThe flash flood-affected wetland region of Bangladesh contributes around 20–25% of the annual Boro rice production, which is vital for local, regional, and national food security. Considering the crucial significance of Boro rice and the growing susceptibility of wetland regions to flash floods and climate change, it is imperative to investigate viable approaches and adaptation methods to reduce the adverse effects on agricultural output. This review study investigated the relationships between wetlands, climate change, and coping mechanisms while incorporating economic and advanced satellite remote sensing approaches. Through a comprehensive analysis of flash flood impacts on Boro rice yield using satellite remote sensing technique, the findings revealed significant adverse effects on the vegetative reproduction of Boro rice in the study areas. Notably, flash floods in 2017 exhibited more pronounced impacts than other years (2018 and 2019), underscoring the urgency for effective and integrated coping strategies to ensure sustainable agricultural production. Consequently, the study identified and discussed probable coping strategies, including changes in cropping patterns through land suitability assessment, evaluating vulnerable areas through vulnerability mapping, analyzing yield forecasting models, and introducing a damage-based crop insurance approach to mitigate farmers’ losses. These probable adaptation strategies will offer viable solutions to address the recurrent issue of flash floods and safeguard sustainable agricultural production in the wetland areas of Bangladesh. Considering the current situation, climate change issues and probable alternative coping strategies are crucial for developing a resilient agricultural system in light of flash floods, ensuring the long-term viability of wetland agriculture. The study outcomes provide valuable insights for policymakers, researchers, and stakeholders in wetland agricultural planning and management to develop proper and long-term sustainable adaptation and mitigation strategies.

Nematode community structure suggests perennial grain cropping cultivation as a nature‐based solution for resilient agriculture

AbstractIntroductionConventional agricultural land‐use may negatively impact biodiversity and the environment due to the increased disturbances to the soil ecosystem by tillage, for example. Cultivation of the perennial grain intermediate wheatgrass (Thinopyrum intermedium, IWG, Kernza®) is a nature‐based solution for sustainable agriculture, improving nutrient retention mainly through its extensive root system. Nematodes serve as sensitive bioindicators, detecting early changes in the soil food web, reflecting in changes in their community structure.Materials and MethodsIWG and annual wheat sites in South France, Belgium and South Sweden were investigated in April 2022 for two depths (5–15 cm; 25–35 cm) to evaluate the difference in nematode community structure among the cropping systems.ResultsSites with IWG cultivation held an accumulation of structure indicators (c‐p 3–5 nematodes) compared to sites with annual wheat cultivation. A generalised linear mixed model revealed significantly more root feeders, especially for the subsoil, under IWG as a result of the perennial cultivation. The maturity index, plant‐parasitic index, channel index and structure index were greater for IWG sites. The enrichment index was greater for annual wheat sites due to the dominance of bacterivores and enrichment indicators (c‐p 1 nematodes). The nematode community structure (weighted faunal profile analysis) indicates IWG sites as being a generally undisturbed system with efficient nutrient cycling and balanced distribution of feeding types, as well as higher metabolic footprint values for root feeders (including plant‐parasitic nematodes) and fungivores. Annual wheat sites, on the other hand, held indicators of a disturbed system with increased occurrence of opportunistic species and a more bacterial driven pathway. The topsoil had an increased occurrence of structure indicators in both cropping systems.ConclusionIWG creates favourable conditions for a diverse food web, including improved nutrient cycling and a heterogeneous resource environment, regardless of climatic conditions, establishing it as a stable and resilient agricultural management system.

Concurrent drought threatens wheat and maize production and will widen crop yield gaps in the future

CONTEXTDrought poses a significant threat to global crop production. As the global community grapples with the escalating challenges of climate change, understanding the multifaceted impacts of concurrent drought on food security becomes imperative. OBJECTIVEThis study delved into the response of wheat and maize, key staples in the global food system, to different types of drought, with a particular focus on the yield gaps resulting from concurrent meteorological and agricultural drought. METHODSThe DSSAT-CERES model was adopted to simulate phenophase, rain-fed, and potential yields of maize and wheat in China from 1962 to 2100. Meteorological (Non-stationary Standard Precipitation Evapotranspiration Index, NSPEI) and agricultural (Standard Soil Moisture Index, SSMI) drought indices were calculated from crop seeding to maturity stages. We employed bivariate and multiple cross-wavelet as well as vine Copula to qualitatively and quantitatively analyze the response of yield gaps to different drought types. Finally, we determined the relative dependence weights of maize and wheat on NSPEI and SSMI by least squares regression. RESULTS AND CONCLUSIONSSpanning from 2022 to 2100, a trend of shortened growth periods for these crops were detected, accompanied by increasingly drier conditions. These situations exacerbated the crops' vulnerability to concurrent drought, leading to considerable yield reductions. Our projections indicated that future yield gaps due to concurrent drought could be, on average, 2–30% higher than those caused by single-type drought. Concurrent drought affected wheat (5–50%) more severely than maize (0–35%). Western regions would be more affected than the Eastern regions. Under the SSP (Shared socioeconomic pathway) 5-8.5 scenario in 2022–2100, all four crops would have higher dependence weights on SSMI (51–99%) than NSPEI (26–59%), emphasizing the critical role of soil moisture in agricultural drought monitoring and yield loss alleviation. SIGNIFICANCEOur findings highlight the urgent need for integrated drought management strategies that address the compounded risks of concurrent drought, thereby contributing to the resilience of agricultural systems and global food security in a changing climate. Our research proposes to consider the relative weights of meteorological and agricultural drought in the future development of composite drought monitoring indicators for addressing food drought risk under climate change.

Determination of irrigation requirement for tomato using FAO-CROPWAT model in the Arid Region

Food security presents a significant challenge in arid regions, exacerbated by water scarcity due to climate change. Tomato is one of the top agricultural crops around the world, in which it is needed to investigate its irrigation water requirements for each region, especially in the arid regions. This work mainly aims to estimate the irrigation water requirements and irrigation schedules for cultivating tomato in Ras Al Khaimah (RAK), United Arab Emirates (UAE). This would be done by utilizing the FAO-CROPWAT 8.0 Model and CLIMWAT 2.0. The findings of this research support efficient water use by utilizing the advanced modeling tools. The outcomes will inform and enhance irrigation water management practices for farmers, farm owners, and policymakers, contributing significantly to sustainable development goals and the resilience of agricultural systems in water-limited environments.

Determination of irrigation requirement for tomato using FAO-CROPWAT model in the Arid Region

Food security presents a significant challenge in arid regions, exacerbated by water scarcity due to climate change. Tomato is one of the top agricultural crops around the world, in which it is needed to investigate its irrigation water requirements for each region, especially in the arid regions. This work mainly aims to estimate the irrigation water requirements and irrigation schedules for cultivating tomato in Ras Al Khaimah (RAK), United Arab Emirates (UAE). This would be done by utilizing the FAO-CROPWAT 8.0 Model and CLIMWAT 2.0. The findings of this research support efficient water use by utilizing the advanced modeling tools. The outcomes will inform and enhance irrigation water management practices for farmers, farm owners, and policymakers, contributing significantly to sustainable development goals and the resilience of agricultural systems in water-limited environments.

A high-quality genome assembly and annotation of Thielaviopsis punctulata DSM102798

Black scorch disease (BSD), caused by the fungal pathogen Thielaviopsis punctulata (Tp) DSM102798, poses a significant threat to date palm cultivation in the United Arab Emirates (UAE). In this study, Chicago and Hi-C libraries were prepared as input for the Dovetail HiRise pipeline to scaffold the genome of Tp DSM102798. We generated an assembly with a total length of 28.23 Mb comprising 1,256 scaffolds, and the assembly had a contig N50 of 18.56 kb, L50 of three, and a BUSCO completeness score of 98.6% for 758 orthologous genes. Annotation of this assembly produced 7,169 genes and 3,501 Gene Ontology (GO) terms. Compared to five other Thielaviopsis genomes, Tp DSM102798 exhibited the highest continuity with a cumulative size of 27.598 Mb for the first seven scaffolds, surpassing the assemblies of all examined strains. These findings offer a foundation for targeted strategies that enhance date palm resistance against BSD, and foster more sustainable and resilient agricultural systems.

A high-quality genome assembly and annotation of Thielaviopsis punctulata DSM102798

Black scorch disease (BSD), caused by the fungal pathogen Thielaviopsis punctulata (Tp) DSM102798, poses a significant threat to date palm cultivation in the United Arab Emirates (UAE). In this study, Chicago and Hi-C libraries were prepared as input for the Dovetail HiRise pipeline to scaffold the genome of Tp DSM102798. We generated an assembly with a total length of 28.23 Mb comprising 1,256 scaffolds, and the assembly had a contig N50 of 18.56 kb, L50 of three, and a BUSCO completeness score of 98.6% for 758 orthologous genes. Annotation of this assembly produced 7,169 genes and 3,501 Gene Ontology (GO) terms. Compared to five other Thielaviopsis genomes, Tp DSM102798 exhibited the highest continuity with a cumulative size of 27.598 Mb for the first seven scaffolds, surpassing the assemblies of all examined strains. These findings offer a foundation for targeted strategies that enhance date palm resistance against BSD, and foster more sustainable and resilient agricultural systems.

Contemporary Farming and Associated Consequences of Climate Change

Aim: This review aims to synthesize current understanding of the multifaceted impacts of climate change on agriculture, examine adaptive strategies to maintain food security, and offer insights into sustainable farming practices. Objectives: The objectives are to: (1) identify the potential causes of climate change, (2) assess its effects on agriculture, and (3) discuss mitigation strategies and sustainable farming practice. Methodology: A systematic review of the literature has been performed. We conducted an in-depth study of previous research, reports, and related literature. Sources were selected based on relevance and credibility to provide a thorough examination of the impacts of climate change on agriculture. Analysis: The analysis focused on categorizing the direct and indirect effects of climate change on agricultural systems, understanding changes in crop yield, plant physiology and metabolism, and evaluating the effectiveness of various mitigation strategies. Result Findings: The findings indicate that the greenhouse effect, driven by gases such as CO2, CH4, and H2O, leads to global temperature increases. The concentration of these gases is rising, with the global average temperature expected to increase by 2°C by 2100, causing significant economic losses. While this increase has boosted plant growth and productivity through enhanced photosynthesis, the associated rise in temperature counteracts these benefits. Therefore, mitigation strategies such as nutrient management, drip and sprinkler irrigation, and sustainable agricultural practices are essential. Natural farming is a sustainable agricultural practice that offers chemical-free, healthy food while promising to increase farmers' income, improve environmental health, restore soil fertility, and reduce greenhouse gas emissions. Conclusion: Climate change poses a significant threat to global food and nutritional security by altering agricultural productivity and sustainability. Understanding these impacts and developing effective mitigation strategies is crucial. This review provides valuable insights for policymakers, researchers, and practitioners to address these challenges and ensure resilient agricultural systems capable of sustaining future food security.

Advancing RNAi-based Strategies for Eco-friendly and Targeted Insect Pest Management in Sustainable Agriculture

The RNA interference (RNAi) has emerged as a promising approach for targeted and eco-friendly insect pest management in sustainable agriculture. The RNAi involves the silencing of specific genes through the introduction of double-stranded RNA (dsRNA), leading to the degradation of complementary mRNA and subsequent reduction in the expression of targeted proteins. This chapter provides a comprehensive overview of the current state and future prospects of RNAi-based strategies for insect pest control. We discuss the molecular mechanisms underlying RNAi, delivery methods for dsRNA, and the design and selection of effective target genes. The application of RNAi in controlling various insect pests, including lepidopterans, coleopterans, and hemipterans is extensively reviewed. also highlight the potential challenges and limitations associated with RNAi-based pest management, such as off-target effects, variable efficacy across insect species, and the development of resistance. Strategies to overcome these challenges, including the use of nanoparticle-based delivery systems and the combination of RNAi with other pest control methods, are explored. Furthermore, discuss the environmental and ecological considerations surrounding the use of RNAi in agriculture, emphasizing the importance of assessing non-target effects and the need for appropriate risk assessment frameworks. The chapter concludes by outlining future research directions and the potential for RNAi to revolutionize insect pest management, contributing to the development of sustainable and resilient agricultural systems.

From drought hazard to risk: A spring wheat vulnerability assessment in the Canadian Prairies

The Canadian drought monitor (CDM) provides monthly assessments of drought severity but does not provide guidance on the potential agricultural impacts. This study extends the CDM to a comprehensive risk assessment framework by assessing the vulnerability of spring wheat to drought timing and severity in the Canadian Prairies. To achieve this, a bayesian neural network (BNN) model was developed that predicts high-resolution wheat yields and uncertainties using the CDM and normalized difference vegetation index (NDVI) data from 2010 to 2021. Model performance is comparable to previous studies, with errors ranging from 13% to 18% of mean yields and R2 values from 0.43 to 0.70 that peak in early August. The model was then applied under various drought conditions to evaluate how different drought timings and severities affect yield vulnerability. The results demonstrate that drought exposure from June to August reduces wheat yields in proportion to drought severity while abnormally dry conditions may have a positive impact of yield, particularly in August. Yield uncertainty decreases substantially from June to July while yield prediction accuracy increases, suggesting that mitigation efforts are most effective prior to July. The final component of the research involved creating operational risk maps that integrate the yield vulnerability assessment with the CDM. These maps were applied in a case study to demonstrate their utility in providing actionable, real-time information for agricultural stakeholders. Overall, this study contributes to the body of knowledge regarding crop sensitivities to varying drought conditions and represents a methodological advancement by enabling high-resolution, operational agricultural risk assessments at scale. By integrating detailed vulnerability data with drought hazard monitoring from the CDM, the information provided by these risk maps can be leveraged by agricultural stakeholders in near real-time to make well-informed decisions that mitigate the impacts of drought on wheat production, enhancing the resilience of agricultural systems to climatic variability.

Strengthening adaptation in coastal Bangladesh: community-based approaches for sustainable agriculture and water management

The coastal region of Bangladesh is significantly influenced by soil and water salinity, which is further exacerbated by the increasing frequency of tropical cyclones and rising sea levels. Understanding the extent of salinity and its challenges is crucial for promoting sustainable agriculture and ensuring access to safe drinking water. Using quantitative (soil and water parameters) and qualitative (focus group discussion and key informant interview) data, we investigated (i) soil and water salinity and soil nutrient contents; and (ii) adaptive practices in agriculture and drinking water management in three sub-districts (Assasuni, Dacope and Morrelganj) in the southwestern coastal region of Bangladesh. Results show that soil salinity levels did not significantly differ among the sub-districts, with Assasuni having slightly higher soil salinity (8.24 dS m-1) compared to Dacope (8.08 dS m-1) and Morrelganj (7.96 dS m-1). Significant differences were observed in the salinity level of pond and canal water among the sub-districts, with Assasuni having the highest levels of salinity in both pond (13.98 dS m-1) and canal water (77.85 dS m-1), compared to other sub-districts. Soil and water salinity were the major challenges reported by the respondents; however, climate-induced stresses (e.g., untimely precipitation) and outbreaks of pests during droughts have been identified as prominent issues in sustainable agriculture. Rainwater harvesting has been identified as a viable adaptive technique in drinking water management, offering a feasible solution to address water and soil salinity. The study underscores the importance of implementing adaptive practices (e.g., rainwater harvesting) to address water scarcity and salinity issues in the coastal region and promote resilient agricultural systems.

Speed Breeding in Cereal Crops: Accelerating Genetic Improvement for Rapid Agricultural Advancement

The global demand for food is rising due to population growth, climate change, and evolving consumer preferences. Traditional plant breeding programs typically require a decade or more to develop and release new crop varieties. Speed breeding is a cutting-edge technique designed to expedite genetic improvement by significantly reducing the seed-to-seed cycle. This review explores the principles, applications, and potential of this method in accelerating agricultural advancement. Originating from NASA's experiments for food production in space, the approach has evolved into a highly efficient strategy for speeding up breeding cycles in cereal crops. By manipulating light, photoperiodic regimes, temperature, and humidity within controlled environments, it is possible to achieve up to six generations of photo-insensitive crops and two to three generations of other crops per year. This rapid generation turnover provides a unique opportunity for accelerated genetic mapping, trait stacking, and enhanced genomic selection. Speed breeding complements modern breeding technologies such as genome editing, high-throughput genotyping, and CRISPR, facilitating quicker development of crop varieties with improved traits. Its applications extend to boosting transgenic pipelines and understanding critical physiological traits in crops. Furthermore, it aligns with breeding methods like single plant selection and single seed descent, offering a more efficient path to desirable outcomes. However, challenges remain, including the need for specialized infrastructure, the impact of genotypic variations, and potential stress responses due to accelerated conditions. Despite these hurdles, speed breeding represents a promising tool in the quest for food security and resilient agricultural systems. This review examines its potential to transform plant breeding, reduce cultivar development times, and contribute to rapid agricultural advancement in a changing world.

Impact of Climate Change on the Farming Community of Bhaktapur District of Nepal

This study was carried out to evaluate the impact of climate change on the farming community in Bhaktapur, Nepal. The study considered rainfall, temperature, flood/drought, harvest and sowing time, and pest and disease incidence over 20 years as primary factors to be considered. A household survey of 120 houses was conducted following the set of questionnaires prepared. As a result, significant changes were observed in climatic aspects as well as biological aspects. The obtained result demonstrated that the precipitation observed drastic change as there was an incidence of erratic and heavy rainfall on summer days while the intensity and frequency of rainfall decreased during winter. These abnormalities certainly do not favor agriculture and farming. Similarly, the temperature of the study area has increased annually over the last two decades. The data suggests that the average annual temperature is increasing at the rate of 0.038°C per year. Moreover, the survey showed that the time of harvest and time of sowing seeds have been delayed respectively to the time a few decades back while the climatic aberrations have increased the incidence of pests and diseases. In a nutshell, the changes in climatic conditions have a drastic change in the farming system and thus the agricultural produce and productivity. The study highlights the significant impact of climate change on traditional farming practices in Bhaktapur, Nepal. It underscores how changes in rainfall patterns, temperature, and the incidence of extreme weather events like floods and droughts have disrupted agricultural activities such as planting and harvesting cycles. The research not only identifies challenges but also emphasizes the need for adaptation strategies to mitigate the impacts of climate change on farming communities. By linking research findings to policy recommendations and practical interventions, the study offers actionable insights for policymakers, practitioners, and local stakeholders striving to build resilience in agricultural systems.

Nitrogen acquisition and retention pathways in sustainable perennial bioenergy grass cropping systems

AbstractPerennial tall grasses show promise as bioenergy crops due to high productivity and efficient nutrient use. Ongoing research on bioenergy grasses seeks to reduce their reliance on synthetic nitrogen (N) fertilizer, the manufacture of which relies on fossil fuel combustion. Excessive use of fertilizers also causes adverse environmental consequences and leads to the evolutionary loss of plant traits beneficial to sustainable N cycle. Notably, perennial tall grasses have exhibited the potential to maintain high biomass yield without the need for N fertilizer or causing soil N depletion. Perennial grasses can be adept at interacting with their microbial partners to facilitate N acquisition and retention via mechanisms such as biological N fixation and nitrification inhibition. These inherent N management traits should be preserved and optimized at the this early stage of bioenergy grass breeding programs. This review examines the impact of external N on bioenergy grass production and explores the potential of leveraging advantageous N‐cycling attributes of perennial tall grasses, laying groundwork for future management and research efforts. With minimized dependency on external N input, the cultivation of perennial energy grasses will pave the way toward more resilient agricultural systems and play a significant role in addressing key global energy and environmental challenges.

Motivating factors of farmers’ adaptation behaviors to climate change in China: A meta-analysis

Adapting to climate change is critical to building sustainable and resilient agricultural systems. Understanding farmers' perceptions of climate change has become the key to the effective implementation of climate change adaptation policies. This research draws multidisciplinary attention to how farmers participate in decision-making on adaptation behaviors and provides useful insights for realizing synergies between environmental change and agricultural production. In this work, we conducted a meta-analysis of 63 quantitative studies on Chinese farmers' adaptation to climate change to assess the relationship between motivational factors and adaptation behavior. Our analysis highlights that farmers' perceptions of precipitation changes are often inaccurate; however, other psychological factors, such as perception, experience, and risk attitude, significantly positively impact their adaptation behavior. In addition, different climate regions are the main source of high heterogeneity in inter-study comparisons of climate change perception, and the effect of climate regions may therefore constitute a moderating factor that weakens the positive relationship between climate change perception and adaptive behavior. Furthermore, this study highlights the need to intervene at the household level to enhance farmers' adaptability to climate change, which includes providing support through income diversification, early warning information services, training, assistance, credit, subsidies, and other resources. In the future, research on how perception, experience, and risk interact to affect adaptive behavior should be strengthened.

Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits

AbstractPlant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency of genetic improvement in future breeding cycles. However, generating the genetic marker data required to apply genomic prediction at the early stages of a breeding program remains costly for many public‐sector breeding programs as well as for many plant breeders operating in developing countries. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties requires plant breeders to make selection decisions without knowing the future environments those crop varieties will encounter in farmers’ fields. Therefore, both lower cost and higher accuracy methods for prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the 21st century. To address this challenge, we conducted linked yield trials of 752 public maize (Zea mays) genotypes in two distinct environments. We developed and trained a phenotypic prediction model to predict yield from manually scored plant traits. The phenotypic prediction approach we employed outperformed genomic prediction in predicting yields in a second environment, with 8.7%–63% higher R2 and 4%–13% less root mean square error than the genomic prediction. The phenotypic prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.

The Influence of Trees on Crop Yields in Temperate Zone Alley Cropping Systems: A Review

Agroforestry is a multifunctional land use system that represents a promising approach to mitigate the environmental impact of agriculture while enhancing the resilience of agricultural systems and ensuring sustainable food production. However, the tree rows in agroforestry systems, particularly in alley cropping systems (ACS), can affect crop productivity on adjacent agricultural fields through various mechanisms. Hence, concerns about declining yields and reduced farm profitability persist and explain the reluctance of farmers to implement ACS on their land. In this review, we examine the available literature on the effects of temperate ACS on yields of various agricultural crops to evaluate if and to what extent crop yields in ACS are affected by tree presence. We identified that ACS crop yields often vary substantially across different species, geographical locations, weather conditions and ACS designs. Our analysis also revealed that several parameters are modified in ACS by the presence of tree rows affecting crop yields positively or negatively and that ACS design aspects play a crucial role in determining crop productivity.

Mapping the race between crop phenology and climate risks for wheat in France under climate change.

Climate change threatens food security by affecting the productivity of major cereal crops. To date, agroclimatic risk projections through indicators have focused on expected hazards exposure during the crop's current vulnerable seasons, without considering the non-stationarity of their phenology under evolving climatic conditions. We propose a new method for spatially classifying agroclimatic risks for wheat, combining high-resolution climatic data with a wheat's phenological model. The method is implemented for French wheat involving three GCM-RCM model pairs and two emission scenarios. We found that the precocity of phenological stages allows wheat to avoid periods of water deficit in the near future. Nevertheless, in the coming decades the emergence of heat stress and increasing water deficit will deteriorate wheat cultivation over the French territory. Projections show the appearance of combined risks of heat and water deficit up to 4years per decade under the RCP 8.5 scenario. The proposed method provides a deep level of information that enables regional adaptation strategies: the nature of the risk, its temporal and spatial occurrence, and its potential combination with other risks. It's a first step towards identifying potential sites for breeding crop varieties to increase the resilience of agricultural systems.

Research priorities to leverage smart digital technologies for sustainable crop production

Agriculture faces several challenges including climate change and biodiversity loss while, at the same time, the demand for food, feed, biofuels, and fiber is increasing. Sustainable intensification aims to increase productivity and input-use efficiency while enhancing the resilience of agricultural systems to adverse environmental conditions through improved management and technology. Recent advances in sensing, machine learning, modeling, and robotics offer opportunities for novel smart digital technologies to enable sustainable intensification. However, developing smart digital technologies and putting them into agricultural practice, requires closing major research gaps, related in particular to (1) the utilization of multi-scale multi-sensor monitoring in space and time, (2) using artificial intelligence for linking process and data-driven methods, (3) improving decision making and intervention in plant production, and finally (4) modeling conditions and consequences of farmers acceptance. Closing these gaps requires an interdisciplinary approach. Here, we present a research agenda and steps forward to steer research efforts, highlighting research priorities, and identifying required interdisciplinary research collaboration. Following this agenda will leverage the full potential of smart digital technologies for sustainable crop production.