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Pesticide exposure, health impacts, predeterminism, and health insurance demand among Pakistani farmers: Implications for policy
No full textThe indiscriminate use of chemical pesticides, regardless of pest infestation, is widespread in the developing world to mitigate agricultural losses. Farmers face significant health risks from pesticides, yet indemnity provision is often neglected in policy discussions. Socio-cultural factors, including religion and trust in governments, can influence indemnity demand, especially among religious communities. However, little to no attention has been given to religious predeterminism and public trust in indemnity service design. We employed a novel approach integrating count data models with contingent valuation to analyze the health impacts of pesticide use and influence of socioeconomic factors, particularly religious predeterminism and public trust, on health insurance demand among farmers in rural Pakistan. Results reveal critical health risks posed by pesticide use among farmers and highlight the limited willingness to pay for health insurance to mitigate these risks. Findings from the Negative Binomial (NB) regression model showed significant positive effects of pesticide quantity (β = 0.607, p < 0.05), WHO Class IA-and-IB pesticides (β = 0.420, p < 0.05), and WHO Class II pesticides (β = 0.277, p < 0.05) on farmers' health. Religious predeterminism and public trust significantly influence farmers' willingness to pay, with only about 27 % of farmers expressing readiness to pay an average of US$4.02 per annum for health insurance. These findings emphasize the importance of tailored health insurance designs that accommodate religious beliefs. Policy initiatives should focus on educating farmers about safe pesticide use and health insurance benefits. Governments can build public trust through subsidized insurance schemes to reduce farmers' out-of-pocket health expenses. The findings emphasize the role of socio-cultural factors, in shaping insurance uptake, suggesting that health insurance policies must be tailored to align with farmers’ belief systems. Government-led initiatives, including subsidized insurance schemes, are essential to enhance public trust, foster safe farming practices, and support sustainable agriculture
QTL mapping for leaf rust resistance in a recombinant inbred line population from the cross of wheat cultivars Zhongmai 578/Jimai 22
No full textLeaf rust is a highly destructive disease that poses a significant threat to both the yield and quality of wheat. Identification of genetic loci can aid in enhancing leaf rust resistance in wheat breeding. In the present study, 262 recombinant inbred lines derived from a cross between Zhongmai 578 and Jimai 22 were used to map leaf rust resistance loci using the Wheat 50K single-nucleotide polymorphism (SNP) array across four environments. Four quantitative trait loci (QTL) on chromosomes 2B (2), 5B, and 7B were identified through composite interval mapping, designated QLr.caas-2B.1, QLr.caas-2B.2, QLr.caas-5B, and QLr.caas-7B, respectively, explaining 3.7%-19.6% of the phenotypic variances. The resistance alleles at QLr.caas-2B.1, QLr.caas-5B, and QLr.caas-7B originated from Zhongmai 578, while that at QLr.caas-2B.2 came from Jimai 22. Both QLr.caas-2B.2 and QLr.caas-5B overlapped with loci previously reported, whereas QLr.caas-2B.1 and QLr.caas-7B are likely to be new loci. Two kompetitive allele-specific PCR (KASP) markers, KASP-QLr.caas-2B.2 and KASP-QLr.caas-5B, were proven to be significantly associated with leaf rust resistance in a diverse panel of 119 wheat varieties mainly from China. Four candidate genes encoding a lectin-receptor kinase protein, F-box family protein, ankyrin repeat domain protein, and putative ABC transporter, respectively, were identified in genetic regions of the four QTL. These findings provide valuable QTL and breeding available KASP markers, facilitating the improvement of leaf rust resistance in wheat through marker-assisted breeding
Recent advances in plant-based green synthesis of nanoparticles: A sustainable approach for combating plant-parasitic nematodes
No full textNanotechnology is emerging as an innovative and sustainable agricultural approach that minimizes environmental impacts by developing nanostructured materials to promote plant growth and combat plant-parasitic nematodes (PPNs). Plant-based nanoparticles (NPs) are attracting increasing attention as they are more environmentally friendly, economical and biocompatible compared to traditional chemical and physical synthesis methods. The ability of plants to reduce and stabilize metal ions and form NPs of specific size and morphology through their biochemical content offers great advantages for agricultural applications. Phytochemicals produced by plants enable the biological synthesis of metal and metal oxide NPs by acting as reducing agents and coating agents in NP synthesis. The effects of plant-based NPs in nematode control are based on mechanisms such as the disruption of the nematode cuticle, induction of oxidative stress and interference with parasite metabolism. Several plant species have been investigated for the synthesis of metal and metal oxide nanoparticles such as silver (Ag-NPs), nickel oxide (NiO-NPs), zinc oxide (ZnO-NPs), copper oxide (CuO-NPs) and iron (Fe-NPs). These biologically synthesized NPs show potent biological activity against important PPNs such as Meloidogyne spp., Pratylenchus spp. and Heterodera spp. The integration of plant-derived NPs into agricultural systems has significant potential for plant growth promotion, nematode suppression and soil health improvement. This review highlights their role in reducing environmental impact in agricultural applications by examining the sustainable synthesis processes of plant-based NPs
CRISPR-mediated genome editing of wheat for enhancing disease resistance
No full textWheat is cultivated across diverse global environments, and its productivity is significantly impacted by various biotic stresses, most importantly but not limited to rust diseases, Fusarium head blight, wheat blast, and powdery mildew. The genetic diversity of modern cultivars has been eroded by domestication and selection, increasing their vulnerability to biotic stress due to uniformity. The rapid spread of new highly virulent and aggressive pathogen strains has exacerbated this situation. Three strategies can be used for enhancing disease resistance through genome editing: introducing resistance (R) gene-mediated resistance, engineering nucleotide-binding leucine-rich repeat receptors (NLRs), and manipulating susceptibility (S) genes to stop pathogens from exploiting these factors to support infection. Utilizing R gene-mediated resistance is the most common strategy for traditional breeding approaches, but the continuous evolution of pathogen effectors can eventually overcome this resistance. Moreover, modifying S genes can confer pleiotropic effects that hinder their use in agriculture. Enhancing disease resistance is paramount for sustainable wheat production and food security, and new tools and strategies are of great importance to the research community. The application of CRISPR-based genome editing provides promise to improve disease resistance, allowing access to a broader range of solutions beyond random mutagenesis or intraspecific variation, unlocking new ways to improve crops, and speeding up resistance breeding. Here, we first summarize the major disease resistance strategies in the context of important wheat diseases and their limitations. Next, we turn our attention to the powerful applications of genome editing technology in creating new wheat varieties against important wheat diseases
Mapping mechanization suitability using machine learning, presence data and spatial co-variates: Methodological approach
No full text14 page
Improving genomic selection in hexaploid wheat with sub-genome additive and epistatic models
No full textThe goal of wheat breeding is the development of superior cultivars tailored to specific environments, and the identification of promising crosses is crucial for the success of breeding programs. Although genomic estimated breeding values were developed to estimate additive effects of genotypes before testing as parents, application has focused on predicting performance of candidate lines, ignoring non-additive genetic effects. However, non-additive genetic effects are hypothesized to be especially important in allopolyploid species due to the interaction between homeologous genes. The objectives of this study were to model additive and additive-by-additive epistatic effects to better delineate the genetic architecture of grain yield in wheat and to improve the accuracy of genomewide predictions. The dataset utilized consisted of 3740 F5:6 experimental lines tested in the K-State wheat breeding program across the years 2016 and 2018. Covariance matrices were calculated based on whole and sub-genome marker data and the natural and orthogonal interaction approach (NOIA) was used to estimate variance components for additive and additive-by-additive epistatic effects. Incorporating epistatic effects in additive models resulted in non-orthogonal partitioning of genetic effects but increased total genetic variance and reduced deviance information criteria. Estimation of sub-genome effects indicated that genotypes with the greatest whole genome effects often combine sub-genomes with intermediate to high effects, suggesting potential for crossing parental lines which have complementary sub-genome effects. Modeling epistasis in either whole-genome or sub-genome models led to a marginal (3%) improvement in genomic prediction accuracy, which could result in significant genetic gains across multiple cycles of breeding
Identifying chickpea (Cicer arietinum L.) genotypes rich in ascorbic acid as a source of drought tolerance
No full textDrought stress induces a range of physiological changes in plants, including oxidative damage. Ascorbic acid (AsA), commonly known as vitamin C, is a vital non-enzymatic antioxidant capable of scavenging reactive oxygen species and modulating key physiological processes in crops under abiotic stresses like drought. Chickpea (Cicer arietinum L.), predominantly cultivated in drought-prone regions, offers an ideal model for studying drought tolerance. We explored the potential of AsA phenotyping to enhance drought tolerance in chickpea. Using an automated phenomics facility to monitor daily soil moisture levels, we developed a protocol to screen chickpea genotypes for endogenous AsA content. The results showed that AsA accumulation peaked at 30% field capacity (FC)-when measured between 11:30 am and 12:00 noon-coinciding with the maximum solar radiation (32 degrees C). Using this protocol, we screened 104 diverse chickpea genotypes and two control varieties for genetic variability in AsA accumulation under soil moisture depletion, identifying two groups of genotypes with differing AsA levels. Field trials over two consecutive years revealed that genotypes with higher AsA content, such as BDNG-2018-15 and PG-1201-20, exhibited enhanced drought tolerance and minimal reductions in yield compared to standard cultivars. These AsA-rich genotypes hold promise as valuable genetic resources for breeding programs aimed at improving drought tolerance in chickpea
Genetic trends in seven years of maize breeding at Mozambique's Institute of Agricultural Research
No full textAssessing genetic gains from historical data provides insights to improve breeding programs. This study evaluated the Mozambique National Maize Program's (MNMP's) genetic gains using data from advanced germplasm trials conducted at 21 locations between 2014 and 2020. Genetic gains were calculated by regressing the genotypic best linear unbiased estimates of grain yield and complementary agronomic traits against the initial year of genotype evaluation (n = 592). The annual genetic gain was expressed as a percentage of the trait mean. While grain yield, the primary breeding focus, showed no significant improvement, significant gains were observed for the plant height (0.67%), ear height (1.74%), ears per plant (1.31%), ear position coefficient (1.22%), and husk cover (4.7%). Negative genetic gains were detected for the days to anthesis (-0.5%), the anthesis-silking interval or ASI (-9.31%), and stalk lodging (-5.01%). These results indicate that while MNMP did not achieve the desired positive genetic gain for grain yield, progress was made for traits related to plant resilience, particularly the ASI and stalk lodging. MNMP should seek to incorporate new breeding technologies and human resources to enhance genetic gains for grain yield and other key traits in the maize breeding program, while developing and deploying high-yielding, climate-resilient maize varieties to address emerging food security challenges in Mozambique
Machine learning algorithms translate big data into predictive breeding accuracy
No full textStatistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability in new lines. Furthermore, ML analyses of genotype-by-environment (G×E) interactions can offer insights into the genetic factors that affect performance in specific environments. By leveraging historical breeding data, ML streamlines strategies and automates analyses to reveal genomic patterns. In this review we examine the transformative impact of big data, including multi-trait genomics, phenomics, and environmental covariables, on genomic-enabled prediction in plant breeding. We discuss how big data and ML are revolutionizing the field by enhancing prediction accuracy, deepening our understanding of G×E interactions, and optimizing breeding strategies through the analysis of extensive and diverse datasets.167-18