Agricultural Food Security Research Articles

School Feeding Programme, Smallholder Farmers, and Development of Rural Areas in Nigeria

Nigerian Federal government under the Muhammadu Buhari led-administration attempts to develop rural areas in Nigeria by introducing the Home-grown School Feeding (HGSF) Programme in 2016 in order to boost local agricultural production, create employment opportunities, reduce out-of-school children, boost nutrition and health status of schoolchildren, and ameliorate economic wellbeing of the rural areas as a strategy to grow and develop the Nigerian Economy. The study examines the linkages among School feeding Programme, smallholder farmers, and rural development in Nigeria. By comparing changes in agricultural productivity between treatment and control groups, the analysis reveals a positive but modest effect of the HGSF programme, with an R-squared value of 0.096 indicating that approximately 9.6% of the variability in agricultural output can be explained by the model. The findings align with existing literature that validates the role of school feeding programmes in enhancing agricultural productivity and food security. The study exposes the nation-wide impact of the policy given the huge investment in a single programme like the HGSF programme since implementation in the 35 states in Nigeria, including the Federal Capital Territory.

Enhanced Tomato Disease Classification Using Hybrid Deep Learning Approach

Tomato disease classification is important for agricultural productivity and food security, yet achieving high accuracy stays challenging due to the complexity of disease symptoms. This study proposed a model classification utilizing DenseNet121 combined with an Autoencoder for feature extraction and Simple neural network (SNN) for classifier. The dataset contains 10 classes of tomato diseases. Experimental results shows that the proposed approach achieved a high accuracy of 99%, outperforming the performance of several approaches. The integration of the Autoencoder enhances feature extraction and dimensionality reduction, important for improved classification outcomes. This study highlights the efficacy of the proposed work in achieving state-of-the-art accuracy in tomato disease detection. Future work may focus on refining the model using advanced augmentation techniques and expanding the dataset to enhance its applicability across diverse agricultural conditions.

Techno-Economic Analysis of BAU-STR Dryer for Rice Drying: An Approach to Accelerate Adoption

Postharvest food loss and waste offset worldwide agricultural productivity and food security. Insufficient drying and storage are the prominent drivers of food loss and waste in underdeveloped countries. Mechanical grain drying systems have distinct benefits over sun drying but are inaccessible to underserved communities due to high capital costs and energy demand. This study evaluated the techno-economic and financial performance of a half-ton-capacity BAU-STR dryer. The moisture extraction rate, drying rate, drying efficiency, and energy consumption were used as technical performance indicators. In contrast, the net present value (NPV), internal rate of return (IRR), benefit–cost ratio (BCR), and payback period were considered economic performance indicators. The technical performance analysis results revealed that the moisture content of rice was reduced from 19.5% to 13.5 ± 0.15% in 4.0 h with an average drying rate of 1.5%/h and a drying efficiency of 75.1%. The financial performance analysis resulted in a drying cost, NPV, IRR, BCR, and PBP of USD 0.96 per 100 kg of grain, USD 3018, 135%, 3.0, and 0.73 yr., respectively, when the annual use was 240 h. If the yearly use of the dryer increased from 240 to 720 h, a higher NPV, IRR, and BCR, as well as a lower payback period and drying cost, could be achieved. Adopting a BAU-STR dryer for drying grain (rice and corn) among underserved communities could play a key role in postharvest food loss and waste.

Assessing the impact of agriculture value chains on food and water environments in Meerut District, India

AbstractWater and food are interconnected elements crucial for agricultural production and food security. While sufficient water resources have facilitated significant food production, intensive agricultural practices have contributed to the degradation of water resources. Agriculture value chains play a pivotal role in shaping food systems and can lead to virtual water movement through the production and trade of water-intensive crops. These chains affect both water and food environments, with water being a driver and food determining factors like availability and accessibility. Despite the economic benefits, agriculture value chains pose non-economic challenges, including environmental and social impacts. The focus on profitability often overlooks sustainability and equity concerns, leading to mono cropping and exclusion of small farmers from markets. This trend is evident in Meerut district, situated in the fertile Indo Gangetic plain and a top farming state in India. Despite its agricultural potential, the district has shifted towards sugarcane monoculture, impacting water and food environments. This study aims to explore the complex relationship between agriculture value chains, water sustainability, food availability and accessibility. The objectives include understanding these interlinkages, analyzing baseline scenarios, and assessing the implications of value chains on food and water environments. The analysis reveals the adverse effects of sugarcane monoculture on water environment, food environment and agricultural sustainability. To address these challenges, the study puts a way forward to adopt value chains approach for the strategical promotion of crop diversification, emphasizing resilience and sustainability.

Ethiopia's Potato Seed System: Regulatory Challenges, Quality Assurance Issues, and Pathways for Improvement ─ A mini review

Background Ethiopia’s potato seed system is currently hindered by a disjointed regulatory framework, insufficient quality assurance processes, and a lack of collaboration among stakeholders, which collectively impede agricultural productivity and food security. The regulatory environment is characterized by inconsistent seed certification practices and a dependence on informal seed sources, negatively affecting the quality of potato seeds available to farmers. Although potatoes play a crucial role in global food security by providing high yields compared to other staple crops, the sector grapples with significant challenges due to bureaucratic inefficiencies, limited technical expertise, and the widespread presence of counterfeit seeds throughout the value chain. Methods To tackle these challenges, this mini-review outlines potential improvements, stressing the necessity for a unified regulatory framework tailored to the unique issues of vegetatively propagated crops. For this reason, we have used different literature source from web of science, Pubmed, Google Scholar, ResearchGate and other scientific websites. We have documented the most relevant information focusing on potato seed system, regulatory frameworks, quality assurance bottlenecks and pathways for improvement across the seed value chain. Results our paper highlights key recommendations that includes enhancing training for regulatory staff, investing in research for disease-resistant varieties, and bolstering public-private partnerships to encourage innovation and resource sharing. Furthermore, improving traceability in the seed supply chain is essential for maintaining seed quality and integrity. Conclusions This review calls for a comprehensive strategy that fosters stakeholder engagement and promotes sustainable practices to rejuvenate Ethiopia’s potato seed system, thereby supporting the livelihoods of millions of smallholder farmers and strengthening national food security through integrated seed sector development and capacity improvement of the seed regulatory body as well as smallholder farmers.

Urban food availability and consumption in Grand Lomé, Togo: A household perception analysis

Introduction: Food well-being of the population is one of the priorities of the Togolese government, which relies on the agricultural investment and food security Programme to increase national food production. In addition, the country relies on food imports to make up the shortfall. At the same time undernourishment and malnutrition remain high among the country’s population. This research analyzes food supply and its implications for household consumption in Grand Lomé, Togo. [Methods] The methodology used documents, a survey of 963 heads of household randomly sampled households and semi-structured interviews with 10 households and with Togolese food safety agency (ANSAT). Quantitative data were processed and analyzed using Excel spreadsheets R and R-Studio, while content analysis was applied to the verbal applied to the verbal statements collected. Results: Firstly, the results show that domestic agricultural production contributed an average of 91% of food supply between 2014–2017. The deficit is made up by food imports, which rose from 13.5% in 2014 to 15.4% in 2017. This translated into an acceptable food energy consumption of 2337 Kcal/head/day in 2017. Secondly, 81% of respondents recognize a strong food presence at consumer markets, except that the chi-square test applied to the data at the 5% threshold shows (p-value < 2.2 × 10−16), indicates that this satisfaction is a function of place of residence. Despite this, persistent shortages affect more staple crops, livestock and dairy products, leading households to deprive themselves and buy food at affordable prices. Finally, we observe non-diversified diets marked by regular consumption of “cereals/legumes”, vegetables and beverages to the detriment of “tubers/roots”, “meat/fish”, “fruit” and “dairy products”. Conclusion: This research shows that food supply, although adequate, is not sufficient to ensure balanced, nutritious and culturally appropriate food consumption by urban households. Recommendations: To meet these challenges, the central government, in collaboration with urban communes and consumer advocates, must mobilize resources to create urban agricultural farms, strengthen food protection systems, distribute staple products directly to households and limit the importation of food that is hazardous to health.

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.

Assessment of Genetic Diversity in Greengram (Vigna radiata L.) Using Metroglyph Analysis

Pulses like greengram are essential for sustainable agriculture and global food security due to their high protein, complex carbohydrates, fiber, vitamins, and minerals. However, their genetic diversity is underexplored, with existing studies mainly focusing on conventional methods. This study aims to estimate the variability parameters for 16 quantitative characters of greengram to classify the genotypes using Metroglyph analysis and assess genetic divergence to identify divergent parents for future hybridization programs using 45 greengram genotypes using Randomized Block Design replicated thrice. Among 45 genotypes of greengram on the basis of mean performance MGG-351(12.959) followed by MGG-347(43.16 q/ha), VBN -2 (41.12 q/ha), GM-3(40.8 q/ha) and MGG-385(39.8 q/ha) were found superior for grain yield. High GCV and PCV is recorded for Harvest Index and Number of primary branches. High heritability coupled with genetic advance as percent of mean are recorded for number of primary branches, harvest index (%), number of seeds per pod, biological yield per plant, seed index, number of pods per plant, number of clusters per plant. On the basis of Metroglyph analysis, the total index score was varied from 24 (SHAKTI) to 38 (SIKHA) with a mean of 31.84. Among the 45 germplasm lines, the genotypes GM-3, MGG-385, SML-1663 and MGG-371were observed as high yielder and identified for higher index score. Scatter diagram plotted with Plant Height and Harvest Index has revealed that 5 clusters could be distinguished on the basis of morphological variation. Cluster -1 was represented by 24 genotypes, Cluster -2 was represented by 10 genotypes, Cluster-3 was represented by 7 genotypes, Cluster-4 was represented by 3 genotype, Cluster -5 was represented by 1 genotype. From those complexes, the germplasm lines, SIKHA and MGG-371 recorded high index score and fell into different cluster, hence used as parents in hybridization for getting desirable transgressive segregants.

Precision Agriculture Technologies for Early Detection of Crop Pests and Diseases

Crop pests and diseases pose significant challenges to agricultural productivity and food security worldwide. Traditional methods for detecting and managing these threats often rely on manual scouting and blanket pesticide applications, which can be labor-intensive, time-consuming, and environmentally harmful. Precision agriculture technologies offer promising solutions for early detection and targeted management of crop pests and diseases. This review article provides a comprehensive overview of the latest precision agriculture tools and techniques for monitoring crop health, detecting pests and diseases, and guiding site-specific interventions. Key technologies discussed include remote sensing, proximal sensing, machine learning, robotics, and Internet of Things (IoT) sensors. The article highlights the potential of these technologies to improve the timeliness, accuracy, and efficiency of pest and disease detection while reducing reliance on chemical inputs. It also discusses the challenges and opportunities for integrating these technologies into current agricultural practices and extension services. The review concludes with recommendations for future research and development to advance precision agriculture solutions for sustainable crop protection.

Towards sustainable agricultural development and food security in East Asia: The role of broad money and banking credits

The existing literature has extensively explored the role of financial development in boosting agricultural output. However, there is a lack of empirical research on the effects of broad money and banking credits on food production in East Asian countries. This study addresses this gap by examining the relationship between financial variables and agriculture in selected East Asian economies from 1995 to 2019, focusing on food security. Our findings indicate that increases in broad money and financial sector credit significantly enhance long-term food security by 0.216% and 0.846%, respectively. Additionally, we observed positive correlations between food security and agricultural inputs: a 1% increase in agricultural land, fertilizer use, and renewable energy adoption improves food security by 0.219%, 0.049%, and 0.146%, respectively. Robustness checks and Granger causality tests further validate these results. This study underscores the critical role of financial resources and agricultural inputs in food security and calls for a reassessment of credit systems to mitigate any adverse effects. These findings provide important insights for policymakers aiming to strengthen food security in East Asia.

Estimation of Canopy Water Content by Integrating Hyperspectral and Thermal Imagery in Winter Wheat Fields

Rapid and accurate estimation of canopy water content (CWC) is important for agricultural water management and food security. Due to the complexity of dynamic changes in water transport during plant growth, estimation of CWC using a single sensor often leads to high uncertainty in the results. Multi-sensor data fusion is one of the solutions to this problem, but suitable spectral preprocessing methods and data fusion methods still need further research. The objectives of this study were to characterize the performance of two varieties at different growth stages under five water stress conditions and screen hyperspectral sensitive spectral bands by using continuous wavelet transform (CWT) and a successive projection algorithm (SPA). Ultimately, the CWC prediction model of winter wheat hyperspectral characteristic bands and thermal imaging information fusion was created using the GRA algorithm. The results showed that canopy temperature parameters and spectral parameters responded significantly to water deficits in winter wheat. Using the CWT-SPA method, a total of 285 hyperspectral feature bands with wavelet decomposition scales ranging from one to eight were selected. The sensitive bands were mainly distributed in the following ranges: 545–561, 746–1348, 1561–1810, and 2122–2430 nm. The GRA algorithm has good multi-source data model fusion capability, and its constructed prediction model based on hyperspectral and thermal image fusion has high accuracy on the canopy water content in winter wheat (R2 = 0.930, RMSE = 5.44%, nRMSE = 7.94%). Compared to the single-feature spectral model (R2 = 0.864, RMSE = 5.92%, nRMSE = 8.63%) and thermal image CWC prediction model (R2 = 0.813, RMSE = 7.22%, nRMSE = 10.49%), the model prediction accuracy based on the GRA algorithm is increased by 7.64% and 13.69%, respectively.

Genome-wide association study uncovers pea candidate genes and metabolic pathways involved in rust resistance.

Pea (Pisum sativum L.) is an important temperate legume crop providing plant-based proteins for food and feed worldwide. Pea yield can be limited by several biotic stresses, among which rust represents a major limiting factor in many temperate and subtropical regions. Some efforts have been made to assess the natural variation in pea resistance to rust, but its efficient exploitation in breeding is limited since the resistance loci identified so far are scarce and their responsible gene(s) unknown. To overcome this knowledge gap, a comprehensive genome-wide association study (GWAS) has been performed on pea rust, caused by Uromyces pisi, to uncover genetic loci associated with resistance. Utilizing a diverse collection of 320 pea accessions, we evaluated phenotypic responses to two rust isolates using both traditional methods and advanced image-based phenotyping. We detected 95 significant trait-marker associations using a set of 26,045 Diversity Arrays Technology-sequencing polymorphic markers. Our in silico analysis identified 62 candidate genes putatively involved in rust resistance, grouped into different functional categories such as gene expression regulation, vesicle trafficking, cell wall biosynthesis, and hormonal signaling. This research highlights the potential of GWAS to identify molecular markers associated with resistance and candidate genes against pea rust, offering new targets for precision breeding. By integrating our findings into current breeding programs, we can facilitate the development of pea varieties with improved resistance to rust, contributing to sustainable agricultural practices and food security. This study sets the stage for future functional genomic analyses and the application of genomic selection approaches to enhance disease resistance in peas.

Perception and Attitude of Consumer Patronage of Ghana-Grown Rice: Evidence from Kumasi, Ghana

Increasing human population, urbanization, and changing consumer trends have contributed to the emergence of rice as the leading staple food in Ghana. Despite its importance in the Ghanaian diet, patronage and consumption of locally grown rice are not yet widespread. This study investigates consumer patronage of locally grown rice in the Kumasi Metropolis, Ghana, and fills a critical gap in understanding the specific factors that influence consumer preferences in urban settings. Data were collected through a cross-sectional survey of 120 respondents across different income categories using a multistage sampling technique and analyzed using descriptive statistics, a Probit model, and Kendall's coefficient of concordance. The study found that cooking quality, palatability, colour, and fragrance were the primary attributes that influenced consumer preferences. Probit analysis revealed that income and the frequency of rice consumption significantly influenced the likelihood of purchasing locally grown rice. Specifically, a higher income decreases, while frequent rice consumption increases the likelihood of choosing local rice. Key constraints identified include perceived high cost, limited availability, and concerns about the quality of rice. This study recommends that local rice producers, agricultural organizations, and government bodies should make efforts to improve the sensory and culinary qualities of locally grown rice through improved processing techniques, increased promotion, and campaigns to ensure the commodity is readily available to consumers. These strategies aim to boost local rice consumption and support agricultural sustainability and food security in Ghana.

Integrating Machine Learning for Enhanced Agricultural Productivity: A Focus on Bananas and Arecanut in the Context of India’s Economic Growth

AbstractAgriculture is one of the sectors that has an important impact, taking into account the problem of sufficient food supply on a global level. The process of predicting the yield of crops is among the most challenging undertakings in the agricultural industry. Agriculture is the main source of income for most developing nations. The purpose of the study is to investigate the significant role that agriculture plays in boosting India's economic growth. Additionally, the research considers the challenges posed by a growing population and a changing environment in terms of agricultural production and food security. The research focuses on analysing the complex characteristics of the agricultural industry, with a particular emphasis on the nutritional importance of tropical fruits, notably bananas and arecanut. These fruits are well-known for their vital nutrients and their role in ensuring world food security. This study acknowledges the importance of sustainable agriculture practices and incorporates sophisticated machine learning algorithms as dynamic tools to forecast crop yields and enhance decision-making processes throughout the crop development cycle. The main aim of this study is to create strong machine learning models and statistical techniques that can accurately predict crop yield by combining a variety of environmental parameters, then assess which models outperform each other. Assist yield projections may provide governments and policymakers with valuable information to make well-informed choices about food security, import–export policies, and resource allocation. It facilitates national- and regional-level food supply planning. The validation method utilises important metrics like R square (R2), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). This present research adds to the continuing discussion on using creative methods to promote sustainable agricultural growth and ensure food security.

Assessing the initial impact of the Russian invasion on Ukrainian agriculture: Challenges, policy responses, and future prospects

The war in Ukraine has caused significant losses to the Ukrainian agricultural sector and threatened global food security. This study aims to comprehensively analyse the impact of the Russian invasion on Ukrainian agriculture in its initial phase, examine the responses of Ukrainian agricultural policy and outline key elements for the post-war development of the Ukrainian agricultural sector. Using a systematic approach that includes thorough data collection and analysis from official sources, the Ukrainian press, relevant legislation and statistical data, our study focuses on the first five months of the conflict, from February 24, 2022 to July 24, 2022, to identify the main challenges during this period. The agricultural policy analysis shows that the Ukrainian government has adopted a reactive approach, including tax simplification, affordable credit, deregulation, financial support for the agricultural sector, reduced prices for inputs and resources to support farmers, and improvements in logistics. Critical factors identified for post-war development include repatriation and agricultural education, support for small and medium-sized farms, integration into global markets, emphasis on organic practices and sustainable development, and digitalization in agriculture. The integration of a systematic overview of the Ukrainian agricultural sector and an analysis of key elements of post-war development provides essential insights for policy-makers and researchers concerned with the impact of war on the agricultural sector and food security.

A comparative analysis of the evaluation system for comprehensive reform of agricultural water prices in China

Water is a fundamental natural resource that affects human and social development, and also plays an important role in modern agricultural production and food security. In China, agricultural water consumption accounts for the majority of the total water consumption. For a long time, China has not fully implemented water pricing and payment for agricultural water, and the lower agricultural water cost have led to the waste of water resource and the rapid conversion of agricultural water to other industries. In order to avoid agricultural water waste and achieve the goal of agricultural sustainable development, the Chinese government has launched a comprehensive reform of agricultural water prices nationwide since 2016. According to the plan, nowadays, the reform will faces overall evaluation and acceptance in 2024 and 2025. However, there is a lack of a relatively unified and flexible nationwide evaluation index system and acceptance measures. In this study, a comparative research of the evaluation system for comprehensive reform of agricultural water prices was developed. Based on existing evaluation and acceptance measures issued by four typical provincial regions, this study compared and analyzed the compliance and similarities and differences of these acceptance measures with national reform policies and requirements. From the aspects of evaluation procedure and index system, a systematic comparative analysis was conducted on the integrity and differences in the four regions. At the same time, suggestions were given for policy-making of national evaluation process and standard. The comparative analysis results showed that the evaluation indices in the four provincial regions have met all the key points of national reform policies and have a complete system, but there were significant differences in the structure of the evaluation index system in different provincial regions. It is recommended to establish a simple, relatively unified and practical national evaluation and acceptance system as soon as possible. In addition, evaluation criteria for individual specific indices should be determined by region based on the differences in economic capacity and agricultural water conservancy facilities conditions.

Seeds and farmers' rights as drivers of change on food systems

In the heart of Kenya's vibrant agricultural landscape lies a fundamental aspect crucial for food security, cultural heritage, and economic resilience: the right to seed. Embedded within the fabric of traditional farming practices, the right to seed encompasses the freedom of farmers to save, exchange, and utilize traditional and farmer-bred seeds. In Kenya, as in many other countries, this right is not only a legal concept but a cornerstone of agricultural sovereignty. However, its preservation faces challenges in the face of globalization, corporate interests, and evolving intellectual property regimes. Yet, amidst these challenges, significant strides are being made, with collaborative efforts at the global level, particularly through initiatives led by the Convention on Biological Diversity (CBD) and the International Treaty on Plant Genetic Resources for Food and Agriculture (ITPGRFA), aimed at promoting and protecting farmers' rights. Every smallholder farmer has the right to safe, nutritious and healthy food and the ability to sustain themselves and their societies with the existing endorsed resources. The concept of farmers’ rights came out as a subject of debate in international agricultural circles, spearheaded by the Food and Agriculture Organisation of the United Nations (FAO). This includes the right to save, use, exchange and sell farm-saved seed and propagating materials, the right to protect traditional knowledge among others. In developing countries, the right to seed is a fundamental aspect of agricultural sovereignty and food security. This still emulates the subject debate from FAO. However, attention in scientific literature and policy circles for both seeds and farmers’ rights are compromised. This paper employs mediation analysis to explore the causal relationship between seed rights, farmers' rights, and food sovereignty, highlighting the pivotal role of smallholder farmers in shaping sustainable food systems. The findings underscore the importance of inclusive policies that prioritize farmers' rights and empower local communities to sustainably manage their seed resources. Key words: Farmer-managed seeds, seed banks, Access, smallholder farmers and benefit-sharing

Extraction of Winter Wheat Planting Plots with Complex Structures from Multispectral Remote Sensing Images Based on the Modified Segformer Model

As one of the major global food crops, the monitoring and management of the winter wheat planting area is of great significance for agricultural production and food security worldwide. Today, the development of high-resolution remote sensing imaging technology has provided rich sources of data for extracting the visual planting information of winter wheat. However, the existing research mostly focuses on extracting the planting plots that have a simple terrain structure. In the face of diverse terrain features combining mountainous areas, plains, and saline alkali land, as well as small-scale but complex planting structures, the extraction of planting plots through remote sensing imaging is subjected to great challenges in terms of recognition accuracy and model complexity. In this paper, we propose a modified Segformer model for extracting winter wheat planting plots with complex structures in rural areas based on the 0.8 m high-resolution multispectral data obtained from the Gaofen-2 satellite, which significantly improves the extraction accuracy and efficiency under complex conditions. In the encoder and decoder of this method, new modules were developed for the purpose of optimizing the feature extraction and fusion process. Specifically, the improvement measures of the proposed method include: (1) The MixFFN module in the original Segformer model is replaced with the Multi-Scale Feature Fusion Fully-connected Network (MSF-FFN) module, which enhances the model’s representation ability in handling complex terrain features through multi-scale feature extraction and position embedding convolution; furthermore, the DropPath mechanism is introduced to reduce the possibility of overfitting while improving the model’s generalization ability. (2) In the decoder part, after fusing features at four different scales, a CoordAttention module is added, which can precisely locate important regions with enhanced features in the images by utilizing the coordinate attention mechanism, therefore further improving the model’s extraction accuracy. (3) The model’s input data are strengthened by incorporating multispectral indices, which are also conducive to the improvement of the overall extraction accuracy. The experimental results show that the accuracy rate of the modified Segformer model in extracting winter wheat planting plots is significantly increased compared to traditional segmentation models, with the mean Intersection over Union (mIOU) and mean Pixel Accuracy (mPA) reaching 89.88% and 94.67%, respectively (an increase of 1.93 and 1.23 percentage points, respectively, compared to the baseline model). Meanwhile, the parameter count and computational complexity are significantly reduced compared to other similar models. Furthermore, when multispectral indices are input into the model, the mIOU and mPA reach 90.97% and 95.16%, respectively (an increase of 3.02 and 1.72 percentage points, respectively, compared to the baseline model).

Signatures of local adaptation and maladaptation to future climate in wild Zizania latifolia

Global climate change poses challenges to agricultural production and food security. Assessing the adaptive capacity of crop wild relatives to future climate is important for protecting key germplasm resources and breeding new crops. We performed population genomics, genotype-environment association analyses, and genomic offset assessment of Chinese wild rice, Zizania latifolia, a crop wild relative and potential new grain crop, based on 168 individuals from 42 populations. We found two genetic lineages in Z. latifolia, corresponding to the south and north of its range, that diverged during the Late Pleistocene. We also identified lineage-specific positively selected genes associated with flower development and flowering, seed shattering, pathogen defense response and cold tolerance. We further found that populations from southeastern China are the most maladapted to future climate and should be prioritized for conservation. Our findings provide important clues for leveraging existing genetic diversity to identify important germplasm resources and create climate-resilient crops.

Innovative upcycling gabions with coal bottom ash for nonpoint source pollution control against climate change

Climate change accelerates extreme temperature fluctuations and rainfall volatility, and shifts in rainfall patterns, rapidly increasing nonpoint source pollution originated from turbid water and soil erosion. The climate changes also pose serious threats to the agricultural environment and food security. This study innovates the conventional control facilities of turbid water and soil erosion in the sloping field, using coal bottom ash as a harmful waste of thermal power generation. Typical gabion and side modified gabion are designed in consideration with geographical characteristics. The adsorption capacity of coal bottom ash for turbidity, suspended solids (SS), total nitrogen (T-N), and total phosphorus (T-P) were increased as the amounts of coal bottom ash increase. At the rate of 20-g coal bottom ash, the turbidity, SS, T-N, and T-P were absorbed by 84.0%, 80.2%, 40.2%, and 53.2%, respectively. The typical gabion (TG) and side modified gabion (SMG) installed in the sloping field also decreased the turbidity, SS, T-N, and T-P by up to 98.1%, 99.4%, 77.6%, and 84.0%, respectively, during the natural rainfall events. The use of coal bottom ash cannot only be an effective upcycling way to control nonpoint source pollution but also be an ecofriendly approach against climate change.