High-yielding Crop Varieties Research Articles

Hidden Hunger in the Age of Abundance: The Nutritional Pitfalls of Modern Staple Crops.

Hidden hunger, characterized by micronutrient deficiencies despite adequate caloric intake, affects over 2 billion people globally, primarily due to deficits in iron, vitamin A, and iodine. This phenomenon underscores a critical paradox in global food security: the Green Revolution, which significantly increased crop production through high-yielding varieties (HYVs) of staple crops, has simultaneously contributed to widespread nutritional deficiencies. This article examines the dual legacy of the Green Revolution, exploring how its emphasis on yield over nutritional quality has led to decreased concentrations of essential micronutrients in staple crops, exacerbating hidden hunger. The extensive use of synthetic fertilizers, while boosting crop yields, has resulted in environmental degradation and economic burdens for smallholder farmers. Additionally, the shift towards dietary monoculture has reduced agricultural biodiversity and increased the prevalence of diet-related non-communicable diseases. Through diverse case studies from India, Zambia, Guatemala, the Philippines, Brazil, Mexico, and Ethiopia, this article illustrates various strategies to combat hidden hunger, including biofortification, multisectoral approaches, and sustainable agricultural practices. This article highlights the necessity for a multifaceted approach that integrates improved agricultural practices, dietary diversity, and supportive policies to enhance food security and public health. By addressing both caloric and nutritional needs, this comprehensive strategy aims to build resilient food systems that ensure a sustainable agricultural future.

Climate Change Adaptation and Mitigation Measures Among Small Holder Farmers for Sustainable Crop Production in Ebonyi State

This study identified climate change adaptation and mitigation measures among small holder farmer for sustainable crop production in Ebonyi State. Three specific purposes and three corresponding research questions guided the study. It adopted survey research design and used multi-stage and purposive sampling techniques to select 108 crop farmers, 36 extension agents and 54 elders giving a total of 198 respondents. Multiple data collection techniques which included questionnaire, oral interview and observation guides were utilized to collect data for the study. Data gathered for the study were analyzed using mean and standard deviation. Results indicated impacts of climate change on crop production as delay in the period of crop production infestation of pests, diseases and weeds depletion in soil nutrients, and poor crop yields. Ethno-science adopted by farmers were change of date in cultivation, early planting multiple cropping, dry planting and use of organic manures. While techno-science measures identified by the study were growing of high yielding crop varieties and application of chemical fertilizers. Mitigation measures adopted by farmers as identified by the study were integrated crop/livestock farming and planting of trees in homesteads. It was then recommended that government should provide farmers with resources with which to adapt and mitigate climate change and farmers should accept to be trained to cope with climate change.

AutoGP: An intelligent breeding platform for enhancing maize genomic selection.

In the face of climate change and the growing global population, there is an urgent need to accelerate the development of high-yielding crop varieties. To this end, vast amounts of genotype-to-phenotype data have been collected, and many machine learning (ML) models have been developed to predict phenotype from a given genotype. However, the requirement for high densities of single-nucleotide polymorphisms (SNPs) and the labor-intensive collection of phenotypic data are hampering the use of these models to advance breeding. Furthermore, recently developed genomic selection (GS) models, such as deep learning (DL), are complicatedand inconvenient for breeders to navigate and optimize within their breeding programs. Here, we present the development of an intelligent breeding platform named AutoGP (http://autogp.hzau.edu.cn), which integrates genotype extraction, phenotypic extraction, and GS models of genotype-to-phenotype data within a user-friendly web interface. AutoGP has three main advantages over previously developed platforms: 1) an efficient sequencing chip to identify high-quality, high-confidence SNPs throughout gene-regulatory networks; 2) a complete workflow for extraction of plant phenotypes (such as plant height and leaf count) from smartphone-captured video; and 3) a broad model pool, enabling users to select from five ML models (support vector machine, extreme gradient boosting, gradient-boosted decision tree, multilayer perceptron, and random forest) and four commonly used DL models (deep learning genomic selection, deep learning genomic-wide association study, deep neural network for genomic prediction, and SoyDNGP). For the convenience of breeders, we use datasets from the maize (Zea mays) complete-diallel design plus unbalanced breeding-like inter-cross population as a case study to demonstrate the usefulness of AutoGP. We show that our genotype chips can effectively extract high-quality SNPs associated with days to tasseling and plant height. The models show reliable predictive accuracy on different populations and can provide effective guidance for breeders. Overall, AutoGP offers a practical solution to streamline the breeding process, enabling breeders to achieve more efficient and accurate genomic selection.

Harnessing AI-Powered Genomic Research for Sustainable Crop Improvement

Artificial intelligence (AI) can revolutionize agriculture by enhancing genomic research and promoting sustainable crop improvement. AI systems integrate machine learning (ML) and deep learning (DL) with big data to identify complex patterns and relationships by analyzing vast genomic, phenotypic, and environmental datasets. This capability accelerates breeding cycles, improves predictive accuracy, and supports the development of climate-resilient, high-yielding crop varieties. Applications such as precision agriculture, automated phenotyping, predictive analytics, and early pest and disease detection demonstrate AI’s ability to optimize agricultural practices while promoting sustainability. Despite these advancements, challenges remain, including fragmented data sources, variability in phenotyping protocols, and data ownership concerns. Addressing these issues through standardized data integration frameworks, advanced analytical tools, and ethical AI practices will be critical for realizing AI’s full agricultural potential. This review provides a comprehensive overview of AI-powered genomic research, highlights the role of big data in training robust AI models, and explores ethical and technological considerations for sustainable agricultural practices.

Genome-Wide Identification and Analysis of Glycosyltransferases in Colletotrichum graminicola.

Corn leaf blight and stem rot caused by Colletotrichum graminicola are significant diseases that severely affect corn crops. Glycosyltransferases (GTs) catalyze the transfer of sugar residues to diverse receptor molecules, participating in numerous biological processes and facilitating functions ranging from structural support to signal transduction. This study identified 101 GT genes through functional annotation of the C. graminicola TZ-3 genome. Subsequent analyses revealed differences among the C. graminicola GT (CgGT) genes. Investigation into subcellular localization indicated diverse locations of CgGTs within subcellular structures, while the presence of multiple domains in CgGTs suggests their involvement in diverse fungal biological processes through versatile functions. The promoter regions of CgGT genes are enriched with diverse cis-acting regulatory elements linked to responses to biotic and abiotic stresses, suggesting a key involvement of CgGT genes in the organism's multi-faceted stress responses. Expression pattern analysis reveals that most CgGT genes were differentially expressed during the interaction between C. graminicola and corn. Integrating gene ontology functional analysis revealed that CgGTs play important roles in the interaction between C. graminicola and corn. Our research contributes to understanding the functions of CgGT genes and investigating their involvement in fungal pathogenesis. At the same time, our research has laid a solid foundation for the development of sustainable agriculture and the utilization of GT genes to develop stress-resistant and high-yield crop varieties.

Первые 100 лет из жизни Донской опытной станции

Based on the studied archival sources, there is presented the data about scientists (breeders, agricultural technicians, geneticists, soil scientists) who worked at the Don Experimental Station from the moment of its foundation in 1924 to the present time. The main task set by the Soviet government for breeders was the development and introduction of new high-yielding, early-maturing, disease- and pest-resistant varieties of oil crops. The historically significant contribution of scientists of the Don Experimental Station in the development of the agro-industrial sphere of the south of the Russian Federation, the creation of new methods, technological techniques and regulations used in breeding and primary seed production of various oil crops (sunflower, oil flax, mustard, castor oil, rapeseed, safflower, sesame, and soybean) is stated. The development of the Don Experimental Station over the course of a century is traced. There was developed a completely new base of the initial breeding material of oil crops in the conditions of the Rostov region. In the context of the reforming the agricultural and industrial complex, the main tasks were: the development of competitive varieties and hybrids of oil crops that meet international standards and the needs of rural producers; the development of effective environmentally protected and resource-saving agricultural practices and the improvement of primary and industrial seed production; the introduction of completed scientific developments into agricultural practice. During the existence of the station, more than 130 varieties, hybrids and lines of sunflower, castor oil, brown and white mustard, oil flax and other oil crops have been developed and introduced into production. More than 100 patents for breeding achievements have been obtained. The varieties and hybrids of the Don Experimental Station are used in the fields of various soil and climatic zones of the Rostov region, as well as in other regions of Russia, in Ukraine, Moldova, and Bulgaria.

Arbuscular Mycorrhizal Fungi-Assisted Phytoremediation: A Promising Strategy for Cadmium-Contaminated Soils.

Arbuscular mycorrhizal fungi (AMF) have been shown to play a major role in regulating the accumulation, transport, and toxicity of cadmium (Cd) in plant tissues. This review aims to highlight the current understanding of the mechanisms by which AMF alleviate Cd toxicity in plants. Cd accumulation in agricultural soils has become an increasing global concern due to industrial activities and the use of phosphatic fertilizers. Cd toxicity disrupts various physiological processes in plants, adversely affecting growth, photosynthesis, oxidative stress responses, and secondary metabolism. AMF alleviate Cd stress in plants through multiple mechanisms, including reduced Cd transport into plant roots, improved plant nutritional status, modulation of organic acid and protein exudation, enhanced antioxidant capacity, and maintenance of ion homeostasis. AMF colonization also influences Cd speciation, bioavailability, and compartmentalization within plant tissues. The expression of metal transporter genes, as well as the synthesis of phytochelatins and metallothioneins, are modulated by AMF during Cd stress. However, the efficacy of AMF in mitigating Cd toxicity depends on several factors, such as soil properties, plant species, AMF taxa, and experimental duration. Further knowledge of the intricate plant-AMF-Cd interactions is crucial for optimizing AMF-assisted phytoremediation strategies and developing Cd-tolerant and high-yielding crop varieties for cultivation in contaminated soils.

Vegetables grafting: Green surgical fusion to combat biotic and abiotic stresses

Vegetables are considered protective foods with high nutritional value and play an important role in subsistence farming, generating more income. Vegetable crops are highly sensitive to weather fluctuations, which impact their growth, flowering and fruit development and ultimately reduce yield. Grafting has become a viable green surgical option to decelerate conventional breeding approaches to enhance resilience to biotic and abiotic stresses. It is the technique of uniting 2 plants with different genetic backgrounds to create a new one, allowing genetic differences to transfer to the scion. This process offers a better alternative to chemical sterilants in mitigating certain soil-borne diseases in vegetable crops. Solanaceous and Cucurbitaceous vegetable grafting is commercially practiced and has a greater impact in farmers’ fields. Grafting is suggested to mitigate environmental changes' negative impact on vegetable quantity and quality by enhancing physiological activities in plants grafted onto rootstocks with potential traits. This method offers insights into stress response mechanisms, improves stress tolerance and enhances vegetable yield and quality. Recent research on vegetable grafting aims to promote sustainable agriculture by offering resilient, high-yielding crop varieties, such as dual-grafted Brimato, suitable for urban and suburban areas. Research is necessary to comprehend the genetic mechanism and physiological process of grafting technology, with a focus on identifying key physiological processes associated with the characteristic features of rootstock.

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.

Harnessing Genomic Resources and Molecular Breeding Techniques for Advancing Crop Resilience and Productivity

Crop improvement and adaptation are critical to ensure global food security in the face of climate change, population growth, and resource limitations. Exploiting genomic resources and molecular breeding techniques offers immense potential to accelerate the development of resilient, high-yielding crop varieties. This review provides an overview of the current state and future prospects of leveraging genomics and molecular breeding for crop improvement, with a focus on major food crops. We discuss key genomic resources such as reference genomes, transcriptomes, and pan-genomes, as well as powerful molecular breeding approaches like marker-assisted selection, genomic selection, and genome editing. Integrating these tools into crop breeding pipelines can greatly enhance the precision and efficiency of developing improved varieties with desirable traits such as abiotic stress tolerance, disease resistance, and enhanced nutritional quality. We also highlight successful examples of applying these techniques in crops like rice, wheat, maize, and legumes. Furthermore, we explore the role of big data, machine learning, and systems biology in extracting actionable insights from the vast genomic data being generated. Finally, we discuss the challenges and opportunities in translating genomic discoveries into improved crop varieties, emphasizing the need for multidisciplinary collaborations, capacity building, and public-private partnerships. Harnessing the power of genomics and molecular breeding will be pivotal in developing climate-resilient crops to feed the growing global population sustainably.

Identifying climatic risks and relevant adaptation strategies for selected smallholder farming regions, Limpopo Province, South Africa

ABSTRACT Due to low adaptive capacities and high reliance on weather-sensitive natural ecosystems for their livelihoods and food production, smallholder farmers are highly vulnerable to risks associated with climate change and variability. Such risks are location-specific; thus, to cope with them, smallholder farmers require tailored adaptation strategies. To support these farmers, this study aimed to identify climatic risks threatening sustainable smallholder farming in the Limpopo Province regions of Gavaza, Ga-Makanye and Giyani. Weather station records spanning up to 1980–2021 were used to identify climatic risks, for the October–April dryland maize growing season. Risks were identified based on temporal trends and/or interannual variability patterns for reference evapotranspiration and a suite of agriculturally relevant rainfall and temperature indices. Based on the identified risks, tailor-made adaptation strategies were devised for application by farmers within the study region. The results revealed the specific risks of growing seasons becoming shorter and increasingly hotter with highly irregular rainfall patterns. To adapt and improve agricultural productivity despite these prevailing climatic risks, farmers within the study region will benefit if they plant drought tolerant, early maturing and higher yielding crop varieties, apply soil water conservation techniques and make use of seasonal and daily weather forecasts to guide their decision-making.

A Dynamic Process-Based Method for Screening Salt-Tolerant and High-Yielding Crop Varieties

Maize, the most important cereal crop worldwide, is moderately sensitive to salt stress. Given the challenges of soil salinization, developing methods to screen and cultivate salt-tolerant maize varieties is vital for enhancing food security. Among the options, process model-based assisted screening is an effective method for faster and more thorough screening of salt-tolerant varieties. In this study, a method for quickly screening salt-tolerant and high-yielding crop varieties is proposed by combining a coupled model of crop growth and water–salt response with fitness function. Then, this method was applied to the saline areas of the Hetao Plain in China to demonstrate its applicability. This study includes three parts. Firstly, field trial data from 10 commonly grown maize varieties in Hetao Plain (i.e., Xianyu 335, Yinyu 238, Jindan 73, Deke 622, DK-815, Heyu 157, Xianyu 1321, Jinrun 919, Tianci 19, and Xianyu 1225) were used to calibrate the model for different maize varieties. Moreover, model accuracy was evaluated using four indexes including the regression coefficient (b), coefficient of determination (R²), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NSE). Secondly, scenario simulations were conducted using the calibrated model by setting nine initial salinity scenarios to simulate daily dynamic crop growth and soil water–salt changes for the 10 maize varieties. Finally, salt-tolerant and high-yielding maize varieties were screened based on the fitness function method during crop growth periods. The results showed that the simulation model was applicable and functioned effectively for all 10 maize varieties in the region, with the determination coefficient (R2) and Nash–Sutcliffe efficiency coefficient (NSE) of simulated plant height and leaf area index being above 0.90. Furthermore, the R2 of soil water content, soil electrical conductivity, and groundwater depth are 0.51, 0.52, and 0.60, respectively. Afterward, the fitness function values were calculated to bridge the linkage between simulated indicators and scenarios to screen varieties step by step according to the predetermined percentage of screening. Jindan 73, Xianyu 1225, and DK-815 were eventually determined as the most suitable salt-tolerant and high-yielding maize varieties. Therefore, the above results show that the proposed method is suitable for saline crop variety screening with flexibility and applicability.

Socio-Economic Characterization, Identification and Prioritization of Major Constraints and Potentials in Gara Ebanu Community Watershed in Sululta District, Ethiopia

Baseline characterization builds necessary foundation for the plan and obtains proper information for elective planning, implementation and monitoring of the research and development endeavors. The objective of the study was to document baseline information on socio-economic for planning and impact monitoring and to identify and document major socio-economic constraints and potential in the watershed. A total of 62 sample households were selected randomly from all farmers engaged in farming activities in the watershed. The average inorganic fertilizer (NPS and Urea) used in the watershed were 75 kg/ha and 75 kg/ha respectively. The response of the respondents showed that the average yield obtained in the watershed was about 10 qt/ha for barley, 12 qt/ha for wheat, 5 qt/ha for faba bean and field pea 6 qt/ha. The major livestock feed type in the watershed were crop residue (93.5%) followed by hay making (91.9%), grazing in the field (80.6%), local beverage by-products (72.6%), concentrates of different type (67.7%), green feed (50%), stubble grazing (41.9%) and improved forage (14.5%). The result of survey shows that the major income sources of the farmers living in the watershed were livestock production (77.4%) followed by crop production (45.2%) and off-farm activities (25.8%). About 71% of the households have encountered high cost and shortage of agricultural inputs followed by low crop productivity (19.4%), crop disease (8.1%) and existence storage pests (1.6%) were the common in the watershed. In the watershed, high cost of agricultural inputs, low crop productivity, crop disease, shortage of animal feed and fodder, inflation, lack of employment opportunity and other income source were addressed as the highest priority issues by the community that are contributing to the crop productivity reductions and low level of their livelihood in the watershed. By considering the addressed problem related to crop production, livestock production and socio-economic, the interventions on introduction and demonstration of improved and high yielding crop varieties that are resistant or tolerant to the already existing and emerging pests to increase production and productivity of crops should be done. In general, immediate short-term actions should be taken particularly participatory integrated watershed management were recommended.

Unveiling Soil Fertility Dynamics in Rawalpindi Region: Implications for Sustainable Agriculture and Fertilizer Strategies

In Pakistan, the adoption of high-yielding crop varieties and intensive farming practices since the commencement of the green revolution has led to a degradation in soil quality and fertility. Particularly in rainfed areas, rapid mineralization of soil organic matter (SOM) has exacerbated widespread nutrient deficiencies. This study, conducted in Rawalpindi district, aimed to evaluate the extent of soil nutrient deficiencies to formulate precise fertilizer recommendations for management of soil health and fertility to obtain sustainable crop production. A comprehensive analysis of 4080 soil samples from various tehsils within Rawalpindi district was carried out. Results revealed that the majority of soils (79%) were classified as loam, with an overwhelming percentage (88%) exhibiting alkaline pH (7.5 to 8.5). Furthermore, 99% of soils had an electrical conductivity (EC) within the safe limit of <4.00 dS m−1. However, more than 96% of soils displayed poor levels of SOM, and deficiency of available phosphorus was observed in 99% of the samples. Conversely, 63% of soils had adequate extractable potassium. These findings underscore that deficiency of SOM, available-P, and alkaline soil pH are the major contributing factors in Rawalpindi region to the poor soil fertility. Therefore, soil fertility management strategies must consider the findings of this study to obtain the maximum agronomic benefits. Furthermore, farmers are recommended to get their soil tested after every three years to revise the nutrient management programs.

A Comprehensive Analysis of Technology in Aeroponics: Presenting the Adoption and Integration of Technology in Sustainable Agriculture Practices

Aeroponics is a soilless farming method in which plants are grown by sprinkling a nutrient-rich fluid over their roots. Aeroponics has changed as a result of technology, which now offers advantages including automated nutrient supply, environmental management, and plant health monitoring. A key area of concern in light of the rising global food demand, climate change, and environmental issues is sustainable agriculture. This article highlights the integration of technology, sustainability, and socio-economic elements in modern agriculture by methodically examining the development of aeroponics practices from conventional ways to innovative modern techniques. When agronomic practices are viewed historically, they show a shift from labor-intensive, antiquated techniques to precision-based, technology-driven processes. Even though they were sustainable, traditional methods frequently had efficiency and scalability issues. A big change came with the arrival of the Green Revolution, which brought synthetic inputs and high-yield crop varieties. However, a reevaluation towards more sustainable practices was spurred by the approaches' long-term ecological implications. Precision agriculture, which makes use of sensor-based monitoring systems, artificial intelligence (AI), satellite and drone technologies, is a major feature of modern agronomy breakthroughs. According to the systematic research, Industry 4.0 and sensor technology are the most often used technologies in aeroponics. Numerous advantages, including time efficiency and sustainability, have come from these technologies. On the other hand, the research focused on power dependence and technical complexity as the primary obstacles in technology-assisted aeroponics.

Selection of Durum Wheat and SSR Markers for Organic Farming in Central Italy Using AMMI Analysis

Durum wheat is one of the main crops in the Mediterranean region, which is characterized as the hotspot of climate change, with large year-to-year weather fluctuations. Although chemical input reduction in agriculture is strongly demanded, as well as healthy food, there is still a lack of stable and high-yielding crop varieties specifically adapted for organic conditions. This study evaluates the performance of fifteen durum wheat varieties in terms of suitability for organic farming in central Italy and assesses the impact of the genotype–environment interaction (GEI) on productive and quality traits. Variety performance was evaluated in field experiments over four successive seasons. In addition, a genotypic diversity analysis of 38 microsatellites associated with traits important for organic farming was performed. The AMMI (additive main effects and multiplicative interaction) stability analysis revealed that the best and most stable genotype regarding quality traits, such as thousand-kernel weight, protein content, and test weight was the ancient variety, Senatore Cappelli. The most stable and high yield was determined for the Fuego, Iride, and Mv-Pelsodur genotypes. Moreover, SSR markers that could be used for plant breeding, targeting organic farming systems based on molecular markers and GEI results, were identified.

Channels and Methods of Communicating Agricultural Innovations to rice farmers in the Northern Region of Ghana

Rice Farmers in Ghana have benefited from technology dissemination programmes of high-yielding crop varieties to enhance their productivity and increase their incomes. The interventions in the rice sector of Ghana have led to a reduction in poverty, and reduced rice importation into the country. Despite the expected gains from the numerous interventions, there are low levels of adoption of improved rice varieties among many farmers in Ghana, partly due to ineffectiveness of the technology dissemination methods. This study examines rice farmers’ perception of the agricultural innovation communication channels and methods used to promote improved rice varieties in northern Ghana. Primary data was randomly collected (through survey, focus group discussions and key informant interviews) from 404 rice farmers and analyzed by percentages, means, standard deviations and explanations. The farmers perceived the innovation communication channels and methods that mostly helped them to adopt improved rice varieties as the effective ones. The empirical results show that fellow farmers, researchers and extension agents, and radio were perceived to be the most effective channels of innovation communication among the rice farmers. Farm and home visits, and demonstration farms, and radio broadcasts were also considered to be the most effective innovation dissemination methods used to promote improved rice varieties in the region. It is therefore necessary that young farmers are targeted through a combination of individual, group and mass media methods, for enhancing innovation dissemination and uptake. We recommend that the Government of Ghana allocates adequate resources to make agricultural extension delivery more effective and reliable.

Genetic diversity studies for yield attributing characters in chickpea (Cicer arientinum L.) germplasm and their clusters

Genetic diversity is a prerequisite for undertaking any crop breeding programe for the development of high yielding crop varieties to enhance the national food production for the food security. Protein is an important component in the human diet, which is obtained for the vegetarian people from the pulses. Chickpea is an important source of protein for the Indian and African population. Therefore, breeding for high yielding chickpea varieties is a continuous process whose success is dependent on the availability of genetic diversity in the chickpea germplasm. In the present studies forty one genotypes of chickpeas were grouped into twelve clusters. The cluster I was with the highest number of genotypes (23) followed by cluster IV (09), II , III, V, VI, VII, VIII, IX, X, XI and XII which had only 1 genotype in each cluster, respectively.The intra cluster distance (D2) ranges from 68.72 to 90.06 while the inter cluster distance (D2) ranges from 49.00 to 1217.31. The maximum inter-cluster distance (D2 = 1217.31) was observed between cluster VI and cluster XII followed by cluster XII and VIII (D2 = 1069.94), cluster VI and cluster X (D2 = 962.24) and cluster V and cluster VI (D2 = 919.90). It was observed that, seed yield per plant (42.20%) contributed highest for divergence followed by plant height (16.83%), 100 seed weight (16.34%), days to maturity (6.83%), number of pods per plant (5.37%), number of primary branches (4.51%), number of secondary branches per plant (3.05%), harvest index (2.20%), number of seeds per pod (2.07%) and days to 50% flowering (0.61%).

Challenges and Alternatives of Herbicide-Based Weed Management

Weeds are the most severe and widespread biological constraint on agricultural production systems and cause damage to cropped and non-cropped lands. They reduce crop yield and degrade the quality of the produce, besides raising the cost of production. The intensification of agriculture in the Green Revolution era attracted chemical fertilizers and dwarf varieties coupled with mono-cropping and irrigation practices, which enhanced crop-associated weeds and the widespread use of herbicides for easy control. Pesticides may kill many organisms, both target and non-target species, in the environment, causing an imbalance in the ecosystem. Despite the significant increase in productivity, the environmental repercussions of industrial agriculture, characterized by the use of high-yielding crop varieties and the extensive application of chemical fertilizers and pesticides, have prompted a quest for more sustainable agricultural practices worldwide. One potential alternative lies in innovative approaches that draw upon ecological insights gleaned from studying natural ecosystems. These approaches aim to create “ecologically intensive” agro-ecosystems. Developing ecologically intensive agro-ecosystems necessitates a deep understanding of the biological dynamics within ecosystems and the integration of traditional agricultural knowledge held by local farmers. Considering the potentiality of appropriate weed management technologies to substantially improve crop productivity, there is an opportunity for the development, popularization, and adoption of effective, economical, and eco-friendly weed management technologies.

Towards sustainable food crop production: Drivers of shift from crop production to mining activities in Ghana's Arable Lands

This study contributes to the observed reduction of arable lands discourse by examining the shift in land use patterns as well as factors influencing farmers' shift from crop production to mining activities. To achieve this, we employed a combination of Geographic Information System (GIS) technology and Cragg's Double Hurdle Econometric Model as analytical tools. Our approach integrates an econometric model of land use with GIS simulations that predict the spatial pattern of land-use change. The results revealed that crop production lands have been converted into artisanal small-scale mining (galamsey) sites, a development which has implications on food security, life on land and effective utilizations of land and water resources. The results also showed that membership in Farmer-Based Organizations (FBOs), access to credit, and low yield of food crops influenced farmers to shift from crop production to participate in mining activities. In addition, the extent of participation in mining was influenced by family size, membership in FBOs, and low crop yield. While mining operations offer substantial economic benefits, it is crucial to strike a balance between land use for mining activities and food crop production. This is because it has the potential to worsen recent hikes in food prices and subsequently leading to rising inflation rates in Ghana. Our findings have implications on achieving multiple SDGs, viz. SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation) and SDG 15 (Life on Land) as minimizing illegal mining helps increase food production as well as protect water bodies and land. We therefore suggest that government should take steps to make credit accessible to farmers by collaborating with financial institutions and also collaborate with Crops Research Institutes to develop high-yielding crop varieties. Future research could focus on exploring the impact of participating in mining activities on the profitability of food crop production in Ghana.