Resilient Agriculture Research Articles

Disaster Preparedness and capacity building for Resilience in Agriculture

Abstract. Agriculture faces unprecedented challenges due to the increasing frequency and severity of natural disasters and extreme weather events. Agriculture sector needs resilience to support food production and livelihoods because it is susceptible to cyclones, floods, droughts, soil erosion, pests, and disease outbreaks. Geospatial technology plays a significant role in disaster management for agriculture, offering tools for preparedness, response, and recovery. This study explores the role of geospatial technology in early warning systems and risk assessment for different types of natural disasters that impact agriculture activity. Sustainable farming practices that are essential for resilience include crop diversity, climate-resilience varieties, and adaptation strategies. Capacity building and training are vital for effective geospatial technology utilization, especially in developing countries like India, where infrastructure and technology access may be limited. Tailored capacity-building programs are essential, emphasizing climate-smart practices, sustainable land management, and post-disaster recovery strategies. Access to financial services and insurance schemes enhances resilience by helping farmers cope with losses and recover from disasters. In order to improve resilience and sustainability in agriculture, geospatial decision support systems make it possible to evaluate alternative planning, optimise resource allocation, and implement adaptive management.

Climate change dynamics in Tunisian agriculture: A rigorous examination of impact, adaptation, and resilience

This study systemically examines the numerous impacts of climate change on agriculture in Tunisia. In this study, we establish an empirical and comprehensive methodology to assess the effects of climate changes on Tunisian agriculture by investigating current climatic patterns using crop yields and socioeconomic variables. The study also assesses the types of adaptation strategies agriculture uses in Tunisia and explores their effectiveness in coping with climate-related adversities. We also consider some resilience factors, namely the ecological aspect and economic and social camouflage pursued by the (very) men in Tunisian agriculture. We also extensively discuss the complex interconnected relationship between policy interventions and community-based adaptations, a crucial part of the ongoing debate on climate change adaptation and resilience in agriculture. The findings of this study contribute to this important conversation, particularly for areas facing similar challenges.

Research on the impact and mechanism of digital economy on China’s food production capacity

Enhancing and strengthening food production capacity has always been a top priority in agricultural research, serving as a cornerstone for ensuring national food security and stable economic development. This study, based on panel data spanning from 2011 to 2021 across 30 provinces in China, delves into the mechanism through which the digital economy impacts food production capacity. Employing a double fixed effect model, a mediation effect model, and a panel threshold model, we uncover several key findings: The digital economy significantly boosts food production capacity, with robustness tests affirming the reliability of our results. Mechanism analysis reveals that the digital economy enhances food production capacity by elevating total factor productivity and bolstering agricultural resilience. The threshold effect underscores that urbanization levels exhibit a single-threshold impact, wherein the influence of the digital economy on food production capacity intensifies upon crossing this threshold. Heterogeneity analysis reveals that the digital economy significantly boosts food production capacity in central and primary grain-producing regions, while its impact is comparatively weaker in the eastern and western regions, as well as in non-primary grain-producing areas. In summary, this research sheds light on the pivotal role of the digital economy in augmenting food production capacity, offering valuable insights into regional variations and thresholds in its impact across China.

Building Agricultural Resilience: Strategies for Climate Change Adaptation

Climate change has a significant impact on India's agriculture, influencing environmental conditions, agricultural techniques, and production. The term "climate-resilient agriculture" refers to technologies, methods, tools, and frameworks aimed at adapting to and mitigating the effects of climate change on agricultural production, food security, and livelihoods. Crop productivity and food security are greatly impacted by climate changes, which calls for creative agricultural methods to maintain food supply while reducing environmental damages. These difficulties are made worse by environmental changes and global population expansion, especially in developing countries. Technological advancements, such as geospatial analysis, and eco-friendly farming technologies, play a crucial role in optimizing land use and enhancing food security. Crop variety adaptation and management of seed, water, land, livestock, soil and nutrients, pest management, risk management, and post-harvest management are examples of effective agricultural adaptation strategies. Technological developments like geographic analysis.

Increasing Yield more than Maintaining Fertile Soils Motivated the Choice of Climate-Smart Soil Technologies among Greater Lira Pigeon Pea Farmers

Pigeon pea farmers in Gerater Lira, Uganda, fight the adverse effects of climate change and deteriorating soil fertility. They are combining different strategies and adopting climate-smart soil technologies (CSS technologies) to maintain fertile soil and increase yields. The study explored how the motive for maintaining fertile soils affected CSS technologies' choice. A sample of 39 farmers participated in laddering interviews. Data was analyzed by the means-end chain (MEC) framework and the centrality index (CI) technique. MEC results indicate that farmers predominantly linked crop diversification, addressed dietary needs, increased yields, and increased incomes. In addition, they paid less attention to maintaining fertile soils. Results of the CI highlight goal priorities by gender subgroups with females aged at least 40 paying more attention to producing food, soil fertility, and improving health, while male farmers of the same age category were inclined to spread production risk. Results further showed that male farmers below 40 years of age tend to produce for markets and benevolent, while their female counterparts tend to maintain soil nutrients. Our overall findings could help in the development of targeted strategies to encourage a wider spread of CSS technology use for climate-smart agriculture. This could enhance agricultural resilience in the face of climate change. We recommend encouraging farmers to apply CSS technologies while considering the long-term effects they might have on soil fertility. we further recommend that farmers intensify residual retention to improve soil fertility without requiring money to purchase inorganic fertilizer.

The Long-Term Agroecosystem Research (LTAR) network: Cross-site transdisciplinary science to support a sustainable and resilient agriculture.

The Long-Term Agroecosystem Research (LTAR) network is a collaborative initiative funded by the U.S. Department of Agriculture, Agricultural Research Service, aimed at advancing sustainable, resilient agriculture through coordinated research conducted on croplands, grazing lands, and integrated crop/livestock systems. Here we provide an overview of the LTAR network, highlighting its vision, mission, initiatives, recent network activities, and future directions. Network-level research is critical for developing contextually relevant solutions to the challenges faced by agricultural producers. Long-term data collection, stakeholder engagement, and the integration of scientific knowledge are needed to enhance agricultural productivity and its resiliency, environmental quality, profitability, and social well-being into the future.

Opinion: Smart nanofertilizers for growth enhancement and stress resilience in agriculture

Smart nanofertilizers (NFs) represent a promising frontier in agricultural technology, offering precise nutrient delivery and stress resilience to enhance crop growth and productivity while minimizing the environmental impact. This opinion article explores the potential of smart NFs in revolutionizing traditional fertilization practices and addressing the challenges facing modern agriculture. By harnessing the unique properties of nanomaterials, smart NFs including noble metal nanoparticles, green NFs, and novel NFs from industrial waste enable targeted nutrient delivery, enhanced nutrient use efficiency, and stress tolerance in plants. However, their widespread adoption faces regulatory, safety, and scalability challenges that require interdisciplinary collaboration and concerted efforts from stakeholders. Despite these hurdles, smart NFs hold immense promise for promoting sustainable agriculture and ensuring food security in frequently changing global climatic conditions.

Al-driven Precision Agriculture: Advancing Crop Yield Prediction

This article explores the integration of Artificial Intelligence (AI) in precision agriculture, focusing on its role in advancing crop yield prediction and improving overall agricultural productivity. It examines three key areas: satellite imagery analysis, soil health monitoring, and weather data integration. The paper discusses how AI techniques such as Convolutional Neural Networks, IoT sensor networks, and ensemble methods are revolutionizing farming practices. It highlights the technical implementations of these technologies, their applications, and their significant impact on yield optimization, resource efficiency, and decision-making in agriculture. The study emphasizes the crucial role of AI in addressing global food security challenges and improving agricultural resilience to climate change.

Assessing the impacts of climate change on high mountain land-based livelihoods: An empirical investigation in District Nagar, Gilgit-Baltistan, Pakistan

Land-based livelihoods in Pakistan's high mountain regions are highly vulnerable to climate change due to the complex interactions between people and their natural environment. This study uses a mixed-method approach to explore how climate change affects land-based livelihoods in the high mountain Nagar District, Pakistan. Data were collected using a structured household survey of 430 randomly selected farmers, supplemented by focus group discussions and key informant interviews. The findings reveal that 87.7 % of farmers have observed negative impacts of climate change, such as increased crop diseases, reduced water for irrigation, and lower crop yields. Bivariate results indicate that factors related to farming practices, such as farming experience and cropping zones, significantly influence farmers' perceptions of impacts. The study emphasizes the urgent need for targeted government intervention and agricultural planning to boost the resilience of farmers in Nagar District. It calls for improved irrigation, crop disease management, and support tailored to high-mountain farming practices. The research highlights the importance of developing adaptation strategies to protect vulnerable farming communities from climate change impacts and supports the need for effective autonomous adaptation measures. This research contributes to a better understanding of climate change impacts on high-mountain agriculture and emphasizes the need to safeguard vulnerable farming communities.

Unlocking Innovation in Crop Resilience and Productivity: Breakthroughs in Biotechnology and Sustainable Farming

Recent advancements in agricultural biotechnology and sustainable practices are revolutionizing crop production and resilience, addressing global food security challenges. This review highlights cutting-edge innovations in plant sciences, including genetic engineering, genome editing technologies, and biotechnological interventions that enhance crop resistance to biotic and abiotic stresses. It discusses the integration of precision agriculture and digital farming tools that optimize resource use and boost productivity, providing data-driven insights for better crop management. Additionally, sustainable agricultural practices such as agroecology, organic farming, and integrated pest management are examined for their potential to reduce environmental impact while maintaining high yield. The review investigates the key practices like conservation tillage, crop rotation, and biological pest control, emphasizing their roles in enhancing soil health, biodiversity, and agricultural resilience. Significant contributions of biotechnological interventions, including genetic engineering and genome editing, are explored for developing stress-resistant and high-yield crop varieties. Moreover, the integration of precision agriculture and digital farming technologies is discussed for their potential to optimize resource utilization and improve crop management through advanced data analytics.

Integration of SPEI and machine learning for assessing the characteristics of drought in the middle ganga plain, an agro-climatic region of India.

Drought, as a natural and intricate climatic phenomenon, poses challenges with implications for both natural ecosystems and socioeconomic conditions. Evaluating the characteristics of drought is a significant endeavor aimed at mitigating its impact on society and individuals. This research paper explores the integration of the Standardized Precipitation Evapotranspiration Index (SPEI) and machine learning techniques for an assessment of drought characteristics in the Middle Ganga Plain, a crucial agro-climatic region in India. The study focuses on evaluating the frequency, intensity, magnitude, and recurrence interval of drought events. Various drought models, including Random Forest (RF), Artificial Neural Networks (ANN), and an ensemble model combining ANN and RF, were employed to analyze and predict drought patterns at different temporal scales (3-month, 6-month, and 12-month). The performance of these models was rigorously validated using key metrics such as precision, accuracy, proportion incorrectly classified, over-all area under the curve (AUC), mean absolute error (MAE), and root mean square error (RMSE). Furthermore, the research extends its application to delineating drought vulnerability zones by establishing demarcations for high and very high drought vulnerability areas for each model and temporal scale. Results indicate that the south-western part of the middle Ganga plain falls under the highly drought-vulnerable zone, which averagely covers 40% of the study region. The core and buffer regions of drought vulnerability have also been identified. The south-western part of the study area is identified as the core region of drought. Ground verification of the drought-vulnerable area has been done by using soil moisture meter. Validation metrics show that the ensemble model of ANN and RF exhibits the highest accuracy across all temporal scales. This research's findings can be applied to improve drought preparedness and water resource management in the Middle Ganga Plain. By identifying high-risk drought zones and utilizing accurate prediction models, policymakers and farmers can implement targeted mitigation strategies. This approach could enhance agricultural resilience, protect livelihoods, and optimize water allocation in this vital agro-climatic region.

The LTAR Croplands Common Experiment at Upper Mississippi River Basin-Ames.

Agricultural systems evolve from the interactions of climate, crops, soils, management practices (e.g., tillage, cover crops, nutrient management), and economic risks and rewards. Alternatives to the corn (Zea mays L.)-soybean [Glycine max (L.) Merr.] (C-S) cropping systems that dominate in the US Midwest may provide more sustainable use of resources, reduce the documented environmental impacts of current C-S systems, and improve production efficiency and ecosystem services. Innovative management practices are needed to offer producers options to increase farm resilience to variable weather conditions and offset negative environmental impacts. In response to this need, the Upper Mississippi River Basin Long-Term Agroecosystem Research network site at Ames, IA, established a cropland experiment in 2016 to investigate an alternative crop management system that includes reduced tillage, cover crops, and right source, right rate, right time, and right place (4R) nitrogen (N) management. The experimental site is located on the Iowa State University Kelley Research Farm in Boone County, IA. Crop, soil, air, and tile drainage water measurements are made throughout the year using published methods for each agronomic and environmental metric. Our goal is to provide quantitative information to farmers, consultants, agribusiness partners, and state and federal agencies to help guide decisions on the effective use of alternative management practices. Future changes in experimental treatments will adopt a knowledge co-production approach whereby researchers and stakeholders will work collaboratively to identify problems, implement research protocols, and interpret results.

The measurement of agricultural disaster vulnerability in China and implications for land-supported agricultural resilience building

Climate change has recently induced an increase in natural disasters worldwide, which has affected agriculture, a sector highly sensitive to climatic alterations. The paper measures agricultural disaster vulnerability of China in the period 2000–2020 by establishing vulnerability assessment system, and examines how agricultural infrastructure correlates with agricultural disaster vulnerability by constructing grey relational analysis model. Research results indicate that China's agricultural disaster vulnerability presents an overall decreasing trend during the research period. This is mainly attributed to the decreased disaster exposure and increased adaptability to disaster. Spatially, the agricultural disaster vulnerability in the northeastern, central and southwestern regions of China is higher than other regions. Agricultural transportation and communication infrastructure are found to be more influential to agricultural disaster vulnerability followed by irrigation and power facilities. Implications are proposed to enhance agricultural resilience to climate disasters in China.

Climate Risk Management among Smallholder Farmers: A Comparative Analysis of Flood-prone Alappuzha and Drought-affected Gondia in India

Climate change poses significant risks to agricultural systems worldwide, particularly in developing countries where smallholder farmers have limited adaptive capacity. This study examines the socioeconomic and demographic factors influencing climate risk management practices among smallholder farmers in two contrasting regions of India: the flood-prone Alappuzha district in Kerala and the drought-affected Gondia district in Maharashtra. Using data collected from 150 rice farmers, the research analyzes economic, social, technical, and physical dimensions shaping adaptive responses. Key findings reveal significant regional disparities in adaptive strategies. Alappuzha farmers exhibit greater resilience due to higher incomes, better compensation mechanisms, and stronger community networks. Their strategies primarily involve strengthening bunds, improving drainage infrastructure, adopting flood-resistant rice varieties, and relying on formal credit for support. Conversely, Gondia farmers face lower adaptive capacity, driven by limited access to credit, inadequate compensation, and weaker institutional support. Their climate risk management approaches include drip irrigation, planting drought-resistant crop varieties, and pursuing income diversification to reduce vulnerability. Education levels, access to insurance, and the use of localized weather information also play crucial roles in shaping adaptive capacity across both regions. The study underscores the need for targeted interventions to strengthen institutional support, expand educational programs, facilitate community networks, and improve access to localized weather information to enhance agricultural resilience to climate risks. These findings provide practical policy recommendations aimed at addressing region-specific challenges and leveraging local strengths to bolster adaptive capacity.

Boosting farm resilience: Integrating vegetables and spices in nutmeg and arecanut agroforestry

Experiments were conducted to develop a suitable intercropping model under a nutmeg and areca nut-based mixed farming system for higher productivity in the Thondamuthur region of Coimbatore district of Tamil Nadu. Coriander cv.CO4, Fenugreek cv.CO2, Okra hybrid Co 4, Cluster beans cv. MDU 1, Vegetable cowpea cv.PKM 1, Lablab (bush type) Co(Gb-16)) and Small onion cv.CO 4 was intercropped under 14-year-old Arecanut and 18-year-old Nutmeg trees. The design followed was RBD with three replications. Results revealed that the growth and yield of intercrops were reduced under intercropping compared to pure cropping. Among the intercrops, okra was the least affected, yielding 19.8t/ha, 7.78% lower than pure cropping. Coriander and fenugreek were the most affected. The yield of coriander and fenugreek was 0.43 and 0.47 t/ha, respectively 24.88% and 25.05% lower than pure cropping. The growth of nutmeg and areca nut trees were also influenced due to intercropping with vegetable cowpeas. The increment in DBH of nutmeg was maximum (0.52 cm) with vegetable cowpea intercropping and minimum (0.33 cm) with coriander intercropping. The increment in DBH of arecanut was maximum (0.42 cm) with vegetable cowpea intercropping and minimum (0.25 cm) with fenugreek intercropping. The B:C ratio was highest (2.17) under small onion intercropping. Small onion cultivation was successful under nutmeg and arecanut-based mixed farming systems.

A Fuzzy Logic Approach to Performance Evaluation of Genomics-Driven Crop Improvement

Genomics-driven crop improvement has emerged as a revolutionary approach to enhance yield, resilience, and nutritional value in agriculture. However, traditional performance evaluation methods often must catch up with agricultural systems' complex, multifaceted nature. This study proposes a fuzzy logic-based framework to assess the performance of genomics-driven crop improvement initiatives. By integrating fuzzy logic, we can effectively manage uncertainty and ambiguity inherent in agricultural data, allowing for a more nuanced evaluation of crop traits, environmental factors, and management practices. The paper begins by elucidating the fundamental principles of genomics-based agriculture and its impact on crop development. It then delves into the theoretical underpinnings of fuzzy logic and its applicability in modeling the multifaceted aspects of crop growth, encompassing genetic, environmental, and physiological factors. Through a synthesis of existing research and empirical data, the paper illustrates the effectiveness of fuzzy logic in capturing the nuances of crop development processes, including germination, flowering, and yield formation. This paper underscores the pivotal role of fuzzy logic analysis in advancing genomics-based agriculture, offering insights into the dynamic interactions shaping crop development and facilitating informed decision-making for sustainable and resilient food production systems in the face of evolving environmental challenges. Results indicate that the fuzzy logic approach enhances the accuracy of performance assessments and facilitates better decision-making in crop management.

Climate-adaptative management strategies for soybean production under ENSO scenarios in Southern Brazil: An in-silico analysis of crop failure risk

CONTEXTSoybeans (Glycine max L.) are a crucial crop for global food security, and the state of Rio Grande do Sul (RS), Brazil, plays a significant role. However, climate instability, particularly water stress (WS), is a major concern in this region, causing large interannual yield variability. OBJECTIVEThis study aims to address this issue using an in-silico approach to: i) characterize WS and spatial patterns and their frequency within ENSO events; and ii) explore climate-adaptative management strategies such as planting dates and maturity groups to mitigate the risk of crop failure and maximize seed yield and profits. METHODSCrop growth simulations were performed testing three soybean maturity groups (MG, 5.0, 5.8, and 6.4) and eight planting dates (from October 5th to January 20th) over 30 years at 187 locations in RS, Brazil, using APSIM Next Generation. Failure risk was calculated as the percentage of simulations that yielded less than the economic break-even soybean yield in a given scenario. RESULTS AND CONCLUSIONSThe simulated yields were clustered into four regions: Northeast, North, Central, and Southwest. Four WS seasonal patterns were then defined (no stress, early stress, late stress, and whole season stress). On average, WS reduced yields up to 2 Mg ha-1 (∼50 % relative to the maximum). WS varied among regions, with the SW experiencing more frequent and severe stress (up to 50 % of whole season stress during La Nina). ENSO events influenced WS frequency, with El Niño events associated with reduced stress and La Niña events to increased stress. The MG 5.0 resulted in a higher probability of failure risk in all regions. Early planting dates resulted in the highest yield variability (up to 5 Mg ha-1). Climate-adaptative management strategies, such as optimizing planting dates and maturity groups, resulted in a 15 % reduction in crop failure. SIGNIFICANCEOur findings provide valuable insights for developing targeted approaches to enhance soybean yield stability, thereby increasing the resilience of agriculture in the face of future climate uncertainties.

Chromosome-level genome assembly and functional annotation of Citrullus colocynthis: unlocking genetic resources for drought-resilient crop development

Main conclusionThe chromosome-level genome assembly of Citrullus colocynthis reveals its genetic potential for enhancing drought tolerance, paving the way for innovative crop improvement strategies.This study presents the first comprehensive genome assembly and annotation of Citrullus colocynthis, a drought-tolerant wild close relative of cultivated watermelon, highlighting its potential for enhancing agricultural resilience to climate change. The study achieved a chromosome-level assembly using advanced sequencing technologies, including PacBio HiFi and Hi-C, revealing a genome size of approximately 366 Mb with low heterozygosity and substantial repetitive content. Our analysis identified 23,327 gene models, that could encode stress response mechanisms for species’ adaptation to arid environments. Comparative genomics with closely related species illuminated the evolutionary dynamics within the Cucurbitaceae family. In addition, resequencing of 27 accessions from the United Arab Emirates (UAE) identified genetic diversity, suggesting a foundation for future breeding programs. This genomic resource opens new avenues for the de novo domestication of C. colocynthis, offering a blueprint for developing crops with enhanced drought tolerance, disease resistance, and nutritional profiles, crucial for sustaining future food security in the face of escalating climate challenges.

The value of indigenous knowledge for enhancing smallholders’ resilience to climate change and food insecurity: a case study of small-scale irrigation system in Niger

ABSTRACT The global south faces challenges in enhancing smallholder farmers' resilience to climate change and food insecurity. Indigenous knowledge and small-scale irrigation (SSI) systems are being promoted as a viable solution to address these challenges. This paper explores how smallholder farmers use local knowledge to establish informal and farmer-led SSI to enhance smallholders' resilience in agriculture and livelihoods. A qualitative research approach with 79 semi-structured interviews was carried out with farmers' households in Tamaske rural community in Southwest Niger, followed by thematic and statistical analysis. The results revealed that an alternative SSI strategy is mostly based on internal agencies rather than externally supported and it serves as a tool to enhance household food and income security amid climate change. The empirical case allows us to assess the value of indigenous knowledge in agriculture by asking how local people can bring together ideas and resources to enhance SSI and build resilience to climate change. The study suggests that the initiation and multiplication of SSI systems can be achieved by farmers' own agencies if appropriate technology and cooperation mechanisms are selected. The policy focus should promote simple, root-based technology as limited government financial support in the global south may hinder this progress.

Enhancing tomato growth and soil fertility under salinity stress using halotolerant plant growth-promoting rhizobacteria

Soil salinization is a critical issue that not only hampers the efficiency and sustainability of global agricultural production but also poses significant challenges to the achievement of sustainable development goals across environmental, economic, and social dimensions. Halotolerant plant growth-promoting rhizobacteria (HPGPR) have the potential to mitigate abiotic stress, foster plant growth, and bolster the stress resistance capabilities of crops. This study conducted the isolation, identification, and characterization of HPGPR originating from a saline-alkali orchard area in northwest China. The efficacy of the isolated bacterial strains was evaluated through potted plant experiments, assessing the growth of tomato plants under in vitro conditions and under varying salinity stress. Ultimately, the study investigated the influence of these HPGPR on soil physicochemical properties, enzymatic activities, and the structure and composition of the microbial community. Upon isolating 12 bacterial strains, we conducted an in vitro assessment of their salt tolerance, ultimately singling out three robust isolates, which exhibited exceptional salt tolerance. Detailed 16S rRNA gene sequencing and meticulous taxonomic evaluation systematically assigned these isolates to Priestia endophyticus GSCK1 (accession number: OR569048), Bacillus atrophaeus GSCK2 (accession number: OR569061), and Serratia fonticola GSCK6 (accession number: OR569062), respectively. These strains exhibited notable biochemical and plant growth-promoting traits, including enzymatic activities and the production of indole-3-acetic acid. They significantly enhanced plant growth metrics and soil fertilities, particularly strain GSCK6, which also reshaped the soil microbial community, augmenting beneficial microbe abundance. The HPGPR treatment notably improved soil pH, nutrient availability, enzymatic activities, and reduced soil electrical conductivity, underscoring their potential in agricultural resilience against salinity. The eco-friendly salt stress mitigation strategy of HPGPR not only enhances soil quality and promotes plant growth by regulating the composition and function of microbial communities, but also provides a novel solution for global agricultural production. This approach is conducive to increasing crop yield and quality, reducing the limitations of saline-alkali land on agricultural production, and promoting food security and sustainable agricultural development.