Search Results (20)

Peach (Prunus persica) is a temperate fruit crop that is widely cultivated in the northern region of Vietnam. This study investigated the genetic diversity of 59 peach accessions collected from northern mountainous provinces in Vietnam using ISSR (inter-simple sequence repeat) markers. The obtained results enabled the evaluation of genetic variation and relationships among peach varieties, which supports breeding programs and germplasm conservation. The analysis identified high levels of polymorphism (92.5%) across six ISSR primers. The accessions were grouped into two main clusters based on a genetic similarity coefficient threshold of 0.674. There were no significant correlations between genetic and geographic distances. The findings underscore the importance of molecular markers like ISSR for identifying genetic relationships and conserving germplasm resources. The results also highlight the potential genetic drift resulting from the trading and exchange of peach varieties among farmers, leading to the creation of regionally named varieties. This study provides valuable insights into the genetic diversity of Vietnamese peaches, supporting efforts to preserve and utilize these resources for breeding and agricultural development. Full article

The tobacco seedling stage is a crucial period for tobacco cultivation. Accurately extracting tobacco seedlings from satellite images can effectively assist farmers in replanting, precise fertilization, and subsequent yield estimation. However, in complex Karst mountainous areas, it is extremely challenging to accurately segment tobacco plants due to a variety of factors, such as the topography, the planting environment, and difficulties in obtaining high-resolution image data. Therefore, this study explores an accurate segmentation model for detecting tobacco seedlings from UAV RGB images across various geomorphic partitions, including dam and hilly areas. It explores a family of tobacco plant seedling segmentation networks, namely, U-Net, U-Net++, Linknet, PSPNet, MAnet, FPN, PAN, and DeepLabV3+, using the Hill Seedling Tobacco Dataset (HSTD), the Dam Area Seedling Tobacco Dataset (DASTD), and the Hilly Dam Area Seedling Tobacco Dataset (H-DASTD) for model training. To validate the performance of the semantic segmentation models for crop segmentation in the complex cropping environments of Karst mountainous areas, this study compares and analyzes the predicted results with the manually labeled true values. The results show that: (1) the accuracy of the models in segmenting tobacco seedling plants in the dam area is much higher than that in the hilly area, with the mean values of mIoU, PA, Precision, Recall, and the Kappa Coefficient reaching 87%, 97%, 91%, 85%, and 0.81 in the dam area and 81%, 97%, 72%, 73%, and 0.73 in the hilly area, respectively; (2) The segmentation accuracies of the models differ significantly across different geomorphological zones; the U-Net segmentation results are optimal for the dam area, with higher values of mIoU (93.83%), PA (98.83%), Precision (93.27%), Recall (96.24%), and the Kappa Coefficient (0.9440) than those of the other models; in the hilly area, the U-Net++ segmentation performance is better than that of the other models, with mIoU and PA of 84.17% and 98.56%, respectively; (3) The diversity of tobacco seedling samples affects the model segmentation accuracy, as shown by the Kappa Coefficient, with H-DASTD (0.901) > DASTD (0.885) > HSTD (0.726); (4) With regard to the factors affecting missed segregation, although the factors affecting the dam area and the hilly area are different, the main factors are small tobacco plants (STPs) and weeds for both areas. This study shows that the accurate segmentation of tobacco plant seedlings in dam and hilly areas based on UAV RGB images and semantic segmentation models can be achieved, thereby providing new ideas and technical support for accurate crop segmentation in Karst mountainous areas. Full article

Cacao (Theobroma cacao L.) is a tropical tree species belonging to the Malvaceae, which originated in the lowland rainforests of the Amazon. It is a major agricultural commodity, which contributes towards the Gross Domestic Product of West African countries, where it accounts for about 70% of the world’s production. Understanding the genetic diversity of genetic resources in a country, especially for an introduced crop such as cacao, is crucial to their management and effective utilization. However, very little is known about the genetic structure of the cacao germplasm from Sierra Leone and Togo based on molecular information. We assembled cacao germplasm accessions (235 from Sierra Leone and 141 from Togo) from different seed gardens and farmers’ fields across the cacao-producing states/regions of these countries for genetic diversity and population structure studies based on single nucleotide polymorphism (SNP) markers using 20 highly informative and reproducible KASP–SNPs markers. Genetic diversity among these accessions was assessed with three complementary clustering methods, including model-based population structure, discriminant analysis of principal components (DAPC), and phylogenetic trees. STRUCTURE and DAPC exhibited some consistency in the allocation of accessions into subpopulations or groups, although some discrepancies in their groupings were noted. Hierarchical clustering analysis grouped all the individuals into two major groups, as well as several sub-clusters. We also conducted a network analysis to elucidate genetic relationships among cacao accessions from Sierra Leone and Togo. Analysis of molecular variance (AMOVA) revealed high genetic diversity (86%) within accessions. A high rate of mislabeling/duplicate genotype names was revealed in both countries, which may be attributed to errors from the sources of introduction, labeling errors, and lost labels. This preliminary study demonstrates the use of KASP–SNPs for fingerprinting that can help identify duplicate/mislabeled accessions and provide strong evidence for improving accuracy and efficiency in cacao germplasm management as well as the distribution of correct materials to farmers. Full article

In the Sahel region of West Africa, land degradation has raised concerns about the threat of desertification, leading to the establishment of the United Nations Convention to Combat Desertification (UNCCD) in 1994. Over time, the focus has shifted from simply combating desertification to a more comprehensive international program focused on preserving the health of our land by offsetting any damage with restoration efforts by 2030 to sustain ecosystem functions and services. This balancing process—which is in line with the Sustainable Development Goals (SDGs)—is known as Land Degradation Neutrality (LDN). We examine Land Degradation Neutrality (LDN) patterns, namely degradation and rehabilitation processes, by integrating participatory mapping with high-resolution satellite imagery with local stories, observations, historical records, and existing studies. The data elicited an understanding of the processes driving land degradation and adaptation strategies among three distinct ethnic groups of crop and livestock farmers in the village of Yallé in southern Burkina Faso. Some of these people were originally from this region, while others moved from places where the land was already degraded. Participants in the study had diverse experiences and perceptions of land degradation, its drivers, and adaptation strategies, which were influenced by their ethnicity, livelihood activities, and life experiences. These differences highlight the impact of cultural and socioeconomic factors on how people view land degradation, as well as the role of local knowledge in managing the environment. The study emphasizes the necessity of incorporating ethnoecological perspectives into projects focused on Land Degradation Neutrality (LDN) to better understand land degradation and improve land management. This integration can significantly contribute to strengthening global sustainability and community resilience. Full article

Crop digital twin is redefining traditional farming practices, offering unprecedented opportunities for real-time monitoring, predictive and simulation analysis, and optimization. This research embarks on an exploration of the synergy between precision agriculture, crop modeling, and regression algorithms to create a digital twin for farmers to augment the concentration and composition prediction-based crop nutrient recovery. This captures the holistic representation of crop characteristics, considering the intricate relationships between environmental factors, nutrient concentrations, and crop compositions. However, the complexity arising from diverse soil and environmental conditions makes nutrient content analysis expensive and time-consuming. This paper presents two approaches, namely, (i) single-nutrient concentration prediction and (ii) nutrient composition concentration prediction, which is the result of a predictive digital twin case study that employs six regression algorithms, namely, Elastic Net, Polynomial, Stepwise, Ridge, Lasso, and Linear Regression, to predict rice nutrient content efficiently, particularly considering the coexistence and composition of multiple nutrients. Our research findings highlight the superiority of the Polynomial Regression model in predicting nutrient content, with a specific focus on accurate nitrogen percentage prediction. This insight can be used for nutrient recovery intervention by knowing the precise amount of nutrient to be added into the crop medium. The adoption of the Polynomial Regression model offers a valuable tool for nutrient management practices in the crop digital twin, potentially resulting in higher-quality rice production and a reduced environmental impact. The proposed method can be replicable in other low-resourced crop digital twin system. Full article

The insecticide known as neonicotinoid has negative impacts on the ecosystem, human health, and the environment; specifically, its effects on the relationship between crop yields and the death rate of natural pollinators, such as bees, affect food security. The active ingredients in neonicotinoids include imidacloprid, clothianidin, thiamethoxam, acetamiprid, sulfoxaflor, and thiacloprid, which are sold under various trade names. For many of the components of these toxic insecticides, patents have been expired; however, farmers and consumers who continue to use these chemicals are unaware of the products’ toxicity and the environmental effects they have. Thus, agricultural industries are required to consider diverse methods to minimize neonicotinoid use in farming operations and move away from the current prevailing methods. In this short review, the negative effects of neonicotinoid use; the toxic components, health effects, and environmental regulations of neonicotinoids; and sustainable methods to minimize their use are examined. Full article

The Asian water plantain, Alisma orientale (Sam.) Juzep, is a traditional Chinese medicinal plant. The dried tubers of the Alisma orientale, commonly referred to as Alismatis rhizome (AR), have long been used in traditional Chinese medicine to treat a variety of diseases. Soil properties and the soil microbial composition are known to affect the quality and bioactivity of plants. Here, we sought to identify variations in soil fungal communities and soil properties to determine which would be optimal for cultivation of A. orietale. Soil properties, heavy metal content, and pesticide residues were determined from soils derived from four different agricultural regions around Shaowu City, Fujian, China, that had previously been cultivated with various crops, namely, Shui Dao Tu (SDT, rice), Guo Shu Tu (GST, pecan), Cha Shu Tu (CST, tea trees), and Sang Shen Tu (SST, mulberry). As fungi can either positively or negatively impact plant growth, the fungal communities in the different soils were characterized using long-read PacBio sequencing. Finally, we examined the quality of A. orientale grown in the different soils. Our results show that fungal community diversity of the GST soil was the highest with saprotrophs the main functional modes in these and SDT soils. Our data show that GST and SDT soils were most suitable for A. orientale growth, with the quality of the AR tubers harvested from GST soil being the highest. These data provide a systematic approach at soil properties of agricultural lands in need of replacement and/or rotating crops. Based on our findings, GST was identified as the optimal soil for planting A. orientale, providing a new resource for local farmers. Full article

The five independent countries of Central Asia, namely Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan, contain one of the richest areas in the world for the specific and intraspecific diversity of temperate fruit and nut tree species. Research was carried out via the collaboration of national research and education institutes with local community-based agencies and farmer communities. Raw data (2014 observations) for almond, apple, apricot, cherry plum, currant, grapevine, pear, pomegranate, and walnut were collected at the household (HH) level across the five countries: Uzbekistan, Kazakhstan, Tajikistan, Turkmenistan, and Kyrgyzstan. A set of models was used, including household variety richness as the dependent variable, to understand the influence of socio-ecological variables on the amount and distribution of crop varietal diversity in the farmers’ production systems. Four variables were included as explanatory variables of variety richness (fixed factors): ecoregion, ethno-linguistic group, management, and abiotic stress. The results show clear evidence that abiotic stress determines a higher richness of intra-specific diversity in the form of local varieties grown by farmers living in climatically unfavorable areas. The results for the studied ecoregions follow the same trend, with ecoregions with harsher conditions displaying a higher positive correlation with diversity. Mild environments such as the Central Asian riparian woodlands show an unexpectedly lower diversity than other harsher ecoregions. Ethno-linguistic groups also have an effect on the level of varietal diversity used, related to both historic nomadic practices and a culture of harvesting wild fruit and nuts in mountainous areas. The home garden management system hosts a higher diversity compared to larger production systems such as orchards. In Central Asia, encouraging the cultivation of local varieties of fruit and nut trees provides a key productive and resilient livelihood strategy for farmers living under the harsh environmental conditions of the region while providing a unique opportunity to conserve a genetic heritage of global importance. Full article

Local agrobiodiversity in remote areas such as the Moroccan High Atlas is poorly studied, despite being of great importance for the sustainability and resilience of mountainous populations. This includes important species such as wheat (Triticum spp.), barley (Hordeum vulgare), fava beans (Vicia faba), peas (Pisum sativum), and alfalfa (Medicago sativa). This study aimed to better understand varietal naming by farmers and the traits they use for assessing the current diversity of the five species, in 22 locations, distributed across three hubs of the High Atlas. The data were provided by 282 Amazigh informants during focus-group discussions, household surveys, and market surveys, with the support of the Diversity Assessment Tool for Agrobiodiversity and Resilience (DATAR). The use of local terminology for variety names and systematically collected morphological, ecological, and use descriptors appears to be a valuable way to assess local intraspecific diversity, and further comparisons with genomic results are recommended. Furthermore, the results also indicate low diversity at the household level, which contrasts with the greater diversity at the community level. Larger areas are still planted with landraces compared to areas planted with modern varieties, although the levels of richness (number) of both landraces and modern varieties are equivalent overall. Many factors influence this diversity: the biophysical characteristics of the sites, the socio-economic and management practices of farmers, and the availability of varietal diversity and of modern varieties or landraces. Although selection processes have reduced the local diversity available for economically important crops, we found that farmers still rely greatly on landraces, which present traits and variability that allow them to adapt to local conditions. Full article

The smallholder farming systems in Sub-Saharan Africa (SSA) are highly diverse and heterogeneous in terms of biophysical and socio-economic characteristics. This study was conducted in upper Eastern Kenya (UEK) to categorize farm households and determine the influence of socio-economic characteristics (SeC) and soil fertility management practices (SFMP) on soil fertility across farms. Conditioned Latin hypercube sampling (cLHS) was performed to determine 69 soil sampling sites within Meru and Tharaka Nithi counties. From each household (whose field soil sample was obtained), data relating to resource endowment and soil fertility management were collected through a household questionnaire survey. Standard laboratory procedures were used to analyse soil samples. Data reduction was performed using categorical principal component analysis (CATPCA) (for SeC and SFMP) and standard principal component analysis (PCA) (for soil properties). Two-step cluster analysis identified three distinct farm categories or farm types (FT), namely, low fertility farms (FT1), moderately fertile farms (FT2), and fertile farms (FT3). The correlation of clusters against soil properties was significant across pH, soil organic carbon (SOC), cation exchange capacity (CEC), available P, plant available K, and exchangeable bases. FT1 had low SOC, pH, CEC and available P (soil characteristics), low usage of fertilizer and manure (soil fertility management), and smaller household size, lower income, and smaller farm size (socio-economic). FT2 had lower SOC (compared to FT3) and available P. In terms of soil fertility management, FT2 had higher cases of fallowing and composting with moderate fertilizer usage. Households in this category had moderate income, family size, and land size (socio-economic). FT3 had relatively high SOC, pH, CEC, and mineral nutrients. This farm type was characterized by high fertilizer use (soil fertility management) as well as larger household size, higher income, and larger farm size (socio-economic). The results indicate the importance of nutrient management in enhancing soil quality. Delineation and characterization of farms based on the various parameters including resource endowment reveal imbalanced farm resource flows, suggesting a need for locally tailored interventions suited for location-specific conditions to facilitate improved targeting of soil fertility-enhancing technologies and sustainable crop production regimes. While fertilizer is one of the most critical inputs for enhancing agricultural production, it is a major contributor to nitrous oxide emissions from agriculture and can have negative environmental effects on soil biota and water sources. Farmers’ knowledge on the use of fertilizer is thus necessary in developing strategies (such as integrated approach) to promote its efficient use and minimize its detrimental impacts. Full article

Papaya (Carica papaya L.) is one of the most important fruit crops grown in tropical and subtropical regions of the world. Papaya leaf curl disease is one of the greatest concerns next to Papaya ring spot disease for India and the world. A survey was conducted during the year 2019 to 2021 for assessing the leaf curl disease incidence in five major papaya-growing districts of Karnataka State, India. The incidence ranged from 10 to 21 percent, with plants expressing typical begomovirus symptoms. Thirty-two virus-infected papaya samples (PLC-1 to PLC-32), collected from different farmer’s fields, gave positive amplification for begomovirus detection. Based on the partial genome analysis, 13 representative papaya leaf curl isolates were selected for complete genome amplification by rolling circle DNA amplification (RCA). The RCA products were cloned, sequenced and analyzed. Based on the analysis and strain classification criteria for begomoviruses, five isolates (PLC-2, 3, 9, 11 and 18) were considered variants of Chilli leaf curl virus (ChiLCV). Isolate PLC-22 is considered a strain of ChiLCV, with 93.5% nt identity sharing. Similarly, isolate PLC-28 is considered a strain of Croton yellow vine mosaic virus (CYVMV), and isolates PLC-25 and PLC-31 were considered as strains of Papaya leaf curl virus (PaLCuV). Among the remaining four isolates, three (PLC-1, PLC-4 and PLC-7) share more than 91% nt identity among them and less than 91% nt identity with all other reported begomovirus isolates. Hence, they are considered to be isolates of the novel begomovirus, and the name Papaya leaf curl Bagalkote virus [India:Karnataka:Bagalkote:Papaya:2021] is proposed. One isolate (PLC-32) is also found to be distinct from all other begomovirus isolates, including the isolates in the current study also considered to be novel begomovirus, for which we propose the name Papaya leaf curl Haveri virus [India:Karnataka:Haveri:Papaya:2021]. The putative recombination analysis of all 13 papaya isolates showed that a major part of the viral genome was likely descended from the begomoviruses reported previously. This is the first report on the diversity and a distribution of the begomoviruses infecting papaya in Karnataka, India. The current investigation results revealed five major papaya-infecting begomoviruses (PaLCuBKV, ChiLCV, PaLCuV, CYVMV and PaLCuHV) in the sampled regions. Full article

Residue management has become a new challenge for Indian agriculture and agricultural growth, as well as environmental preservation. The rice-wheat cropping system (RWCS) is predominantly followed cropping system in the Indo-Gangetic plain (IGP), resulting in generating a large volume of agricultural residue. Annually, India produces 620 MT of crop residue, with rice and wheat accounting for 234 MT of the surplus and 30% of the total. Farmers are resorting to burning crop residue due to the short window between paddy harvest and seeding of rabi season crops, namely wheat, potato, and vegetables, for speedy field preparation. Burning of residues pollutes the environment, thus having adverse effects on human and animal health, as well as resulted in a loss of plant important elements. This problem is particularly prevalent in rice-wheat-dominant states such as Punjab, Haryana, Uttarakhand, and Uttar Pradesh. If we may use in situ management as residue retention after chopper and spreader, sowing wheat with Happy seeder/zero drill/special drill with full residue load, full residue, or full residue load incorporation with conventional tillage, burning is not the sole approach for residue management. In addition, off-farm residues generated are being utilized for animal feed and raw materials for industries. While there are regional variations in many mechanization drivers and needs, a wide range of mechanization components can be transported to new places to fit local conditions. This article focuses on innovations, methods, and tactics that are relevant to various mechanization systems in particular geographical areas. This article also stresses the need for a thorough analysis of the amount of residue generated, residue utilization using modern mechanical equipment, and their positive and negative effects on crop yield and yield attributes, weed diversity, soil physic-chemical, biological properties, beneficial, and harmful nematode populations in the IGP, which will aid researchers and policymakers in farming research priorities and policy for ensuring sustainability in RWCS. Full article

Sorghum (Sorghum bicolor [L.] Moench) is a valuable crop in the dry regions of the world, including Namibia. Due to the intensity and recurrence of drought and heat stress in the traditional sorghum growing areas, there is a need to breed and deploy new generation farmer-preferred and climate-smart cultivars to serve the diverse value chains. Therefore, the objectives of this study were to assess the present state of sorghum production in northern Namibia and document farmers’ perceived production constraints and trait preferences in new varieties to guide drought-tolerance breeding. A survey was conducted using a participatory rural appraisal in the following six selected sorghum-growing constituencies in Namibia: Kapako and Mpungu (Kavango West Region), Eenhana and Endola (Ohangwena Region), and Katima Mulilo Rural and Kongola (Zambezi Region). Data were collected using a structured questionnaire involving 198 farmers in 14 sampled villages across the regions. Results revealed variable trends in sorghum production among respondent farmers when disaggregated by gender, age, number of households, education level, cropping systems, types of varieties grown, and perceived production constraints. An equal proportion of male and female respondent farmers cultivate sorghum, suggesting the value of the crop to both genders in Namibia. Most respondent farmers (63.6%) were in productive age groups of <40 years old. In the study areas, low-yielding landrace varieties, namely Ekoko, Okambete, Makonga, Kamburo, Nkutji, Katoma, Fuba, Dommy, Kawumbe, and Okatombo, were widely cultivated, and most of the farmers did not use chemical fertilizers to cultivate sorghum. Farmers’ perceived sorghum production constraints in the study areas included recurrent drought, declining soil fertility, insect pest damage, high cost of production inputs, unavailability of improved seed, lack of alternative improved varieties with farmers’ preferred traits, lack of organic manure, limited access to market and limited extension service. The key farmers’ preferred traits in a new sorghum variety included high grain yield, early maturity, and tolerance to drought, in the field and storage insect pests. The study recommends genetic improvement and new variety deployment of sorghum with the described farmers-preferred traits to increase the sustainable production of the crop in Namibia. Full article

Crop diversity conservation in situ is an ecosystem service with benefits at household, community, and global scales. These include risk reduction and adaptation to changing physical and sociocultural environments—both important given the accelerating changes in climate, human migration, and the industrialization of agriculture. In situ conservation typically occurs as part of small-scale, traditionally based agriculture and can support cultural identity and values. Although decisions regarding crop diversity occur at the household level, few data detail the household context of in situ crop diversity management. Our research addressed this data gap for maize and Phaseolus bean in Oaxaca, Mexico, a major center of diversity for those crops. We defined diversity as farmer-named varieties and interviewed 400 farming households across eight communities in two contrasting socioecological regions. Our research asked, “In a major center of maize and Phaseolus diversity, what are the demographic, production, and consumption characteristics of the households that are stewarding this diversity?” We describe the context of conservation and its variation within and between communities and regions and significant associations between diversity and various independent variables, including direct maize consumption, region, and marketing of crops. These results provide a benchmark for communities to understand and strengthen their maize and bean systems in ways they value and for scientists to support those communities in dynamically stewarding locally and globally significant diversity. Full article

Farming areas are made up of diverse land use types, such as arable lands, grasslands, woodlands, water bodies, and other surrounding agricultural architectures. They possess imperative economic value, and are considerably valued in terms of farmers’ livelihoods and society’s flourishment. Meanwhile, detecting crops in farming areas, such as wheat and corn, allows for more direct monitoring of farming area production and is significant for practical production and management. However, existing image segmentation methods are relatively homogeneous, with insufficient ability to segment multiple objects around the agricultural environment and small-scale objects such as corn and wheat. Motivated by these issues, this paper proposed a global-transformer segmentation network based on the morphological correction method. In addition, we applied the dilated convolution technique to the backbone of the model and the transformer technique to the branches. This innovation of integrating the above-mentioned techniques has an active impact on the segmentation of small-scale objects. Subsequently, the backbone improved by this method was applied to an object detection network based on a corn and wheat ears dataset. Experimental results reveal that our model can effectively detect wheat ears in a complicated environment. For two particular segmentation objects in farming areas, namely water bodies and roads, we notably proposed a morphological correction method, which effectively reduces the number of connected domains in the segmentation results with different parameters of dilation and erosion operations. The segmentation results of water bodies and roads were thereby improved. The proposed method achieved 0.903 and 13 for $mIoU$ and continuity. This result reveals a remarkable improvement compared with the comparison model, and the continuity has risen by 408%. These comparative results demonstrate that the proposed method is eminent and robust enough to provide preliminary preparations and viable strategies for managing farming area resources and detecting crops. Full article