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Genotype by environment interaction, path analysis, and yield stability of climate-resilient DroughtTEGO maize hybrids
No full textThe assessment of yield stability of climate-resilient DroughtTEGO® maize (Zea mays L.) hybrids is vital for the productivity and sustainability of maize production in Sub-Saharan Africa. This study evaluated the performance and environmental stability of DroughtTEGO® maize hybrids at multiple locations in Nigeria. Twenty one hybrids plus four checks were planted in an alpha lattice design with three replications for two years in nine locations. A combined analysis of variance revealed highly significant (P < 0.01) differences for most traits across years and locations, indicating the influence of environmental factors on the hybrid performance. Genotypic variability was observed for traits such as grain yield, plant height and flowering time, with hybrid × environment interactions significantly affecting hybrid rankings. Grain yield ranged from 3421 to 5808 kg ha⁻¹, with hybrid WE5229 outperforming commercial checks by 22.6%. Path analysis indicated that the number of ears per plant had the highest positive direct effect on yield, whereas ear aspect had the greatest negative impact. The GGE biplot analysis showed that PC1 and PC2 explained 53.41% of the total variation in grain yield, with WE9216 emerging as the most stable and highest-yielding hybrid across the locations. Ibadan and Birnin Kudu were identified as the best testing environments, whereas the other locations were useful for culling unstable hybrids. These results suggest that DroughtTEGO® hybrids, WE9216 and WE5229, are well-suited for commercialization in Nigeria. This study emphasizes the importance of multi-environment testing for identifying high-yielding and stable hybrids adapted to specific agro-ecologies.181-19
Optimization of sowing time to mitigate heat stress in spring maize (Zea mays) in Indo-Gangetic plains of India
No full textIn spring maize (Zea mays L.) prone to heat stress, especially at terminal stages, understanding the impact of sowing time on important genotypes for heat stress tolerance is crucial to optimize yield. An experiment was conducted during 2020 and 2021 at the Research farm of ICAR-Indian Institute of Maize Research, Ludhiana, Punjab to study the effect of sowing time and genotype interactions on yield and heat stress in spring maize. The experiment was laid out in a split-plot design (SPD) comprised of 4 different sowing dates, viz. 15th February; 25th February; 5th March; and 15th March, and 4 maize genotypes, viz. PMH1; PMH10; CoH(M)6; and CoH(M)8, replicated thrice. Spring maize sown on 15th February gave a higher grain yield (8.5 t/ha). Successive delays of 10, 20, and 30 days in sowing of spring maize caused significant yield penalties of 15%, 24%, and 29%, respectively. Heat stress at flowering was observed with delayed sowing (5th and 15th March), leading to a ~20% yield decline compared to non-stressed conditions (15th February). Furthermore, sowing beyond 15 February resulted in a shortening of vegetative (4–15 days) and reproductive (3–8 days) periods. Spring maize sown on 15 February gave higher water productivity (16–34%) compared to delayed sowings. Among genotypes, PMH 1 recorded a higher yield (8.2 t/ha) under non-stressed conditions with early sowing on 15th February. However, under heat stress, PMH 10 gave a higher yield (6.5 t/ha) sown on 25th February. Overall, it could be concluded that spring maize sowing up to 15th February is the optimum time to avoid heat stress at the flowering stage to achieve higher yield in north-western regions of India.22–2
Dataset on the patterns of livelihood diversification in farming systems of the Eastern Gangetic Plains of South Asia
No full textThe Eastern Gangetic Plains (EGP) region, characterized by smallholder-dominated farming, is experiencing rapid socio-economic and environmental changes. To enhance resilience, income stability, and food security, smallholders are increasingly diversifying their livelihoods away from traditional agriculture. However, the patterns and drivers of this diversification remain poorly understood. This study, utilizing data from the Rupantar project, aims to elucidate these patterns, identify key drivers, and assess the impacts on productivity, profitability, nutrition, and inclusion. A mixed-methods approach was employed, including a baseline survey of 1400 households across India, Nepal, and Bangladesh and analysis using the Simpson's Index of Diversity (SID). Fractional regression models revealed moderate diversification levels across the EGP with significant geographical and contextual variability. Key drivers included access to resources, gender, education, market access, and institutional support, with differences observed across countries and diversification types. Factors such as non-ownership of irrigation pumps, female household headship, and engagement in off-farm activities were significant predictors of higher diversification. The study found that diversification can enhance income security, nutritional outcomes, and environmental sustainability, although impacts vary by diversification type
The emergence of microbiological inputs and the challenging laboratorisation of agriculture: lessons from Brazil and Mexico
No full textIn this article, we analyse the tensions associated with the emergence of microorganism-based agricultural inputs in two Latin American countries, Brazil and Mexico. More specifically, we examine the ways in which these technologies, which are based on the use of living organisms, leave public microbiology research laboratories and are further developed by manufacturers or farmers. To this end, we draw on the concept of the ‘laboratorisation’ of society, part of the actor-network theory. We show that the emergence of these technologies is currently facing a number of challenges, due to the risks associated with their biological nature and the difficulty involved in establishing production processes as reliable as those used in reference laboratories. Whether produced by companies or on farms, the quality and safety of the practices and of these products are the subject of debate, as well as the focus of scientific, economic and political scrutiny. These microbiological inputs are evidence for the transformation of the relationship between science, industry, users and politics that is taking place around the emergence of alternatives to synthetic chemical inputs in agriculture, and more broadly, about the use of microbiological resources in agriculture.369–38
Drivers of soil organic carbon stocks at village scale in a sub-humid region of Zimbabwe
No full textLand use change caused by agriculture and inappropriate agricultural management cause soil organic carbon (SOC) loss. This study was conducted in a smallholder communal area of Zimbabwe with the following objectives: i) to quantify SOC stocks under contrasting land uses and soil types, and estimate landscape-level SOC stocks, ii) to assess the impact of historical agricultural management parctices on SOC in croplands (homefields vs outfields), and iii) to estimate temporal changes in SOC stocks due to land use change using field measurements and geospatial data (Africa Soil Information Service, AfSIS). SOC stocks were measured across three soil types and eight land uses (croplands, gardens, fallows, grasslands, vleis, shrublands, forests and tree plantations) at soil depths of 0–20 and 20–40 cm. Estimates from AfSIS were also used for comparison. SOC stocks were highest on black clay soils (66.9 ± 2.30 Mg C/ha), followed by red clay soils (36.1 ± 2.04 Mg C/ha) and sandy soils (25.5 ± 0.59 Mg C/ha). Among land uses, SOC stocks were highest in vleis (67.9 ± 3.55 Mg C/ha), followed by gardens (56.4 ± 2.34 Mg C/ha) and grasslands (53.1 ± 6.18 Mg C/ha). Croplands on sandy soils had the lowest stocks (22.7 ± 0.77 Mg C/ha). Distance from homestead had no significant effect on SOC stocks. SOC stocks estimated by AfSIS were systematically underestimated in vleis, grasslands and gardens, resulting in a 20 % underestimation of landscape SOC stocks. Landscape SOC stocks declined slightly (−0.2 %) from 2002 to 2023, though the change was not statistically significant. Our findings highlight that SOC stocks hotspots are concentrated in vleis, gardens and grasslands, mostly within communal grazing lands. Their conservation should therefore be a priority, emphasizing the need for collective management. On the other hand, restoration of degraded croplands could be enhanced by strenghtening linkages between cultivated fields and communal grazing lands through improved livestock management
Dry direct-seeded and broadcast rice: a profitable and climate-smart alternative to puddled transplanted aus rice in Bangladesh
No full textContext: Dry direct-seeded rice (DSR) has been identified as a potential crop establishment method to reduce labor, water, and energy use, as well as the carbon footprint and is considered as a climate-smart practice for rice production. However, the economic feasibility and farmers’ adoption of DSR will likely depend on its productivity compared to the dominant practice of puddled transplanted rice (PTR). Tillage and crop management practices, landscape position, and rice cultivars are also likely to influence DSR productivity, profitability, energy use, and global warming potential (GWP). While numerous studies have compared the performance of DSR with PTR, none have evaluated DSR across different landscape positions to identify the most suitable landscape for expansion of DSR. Methods: We conducted multilocation and multi-year trials comparing the performance of spring ‘aus’ season rice establishment methods (machine drilled DSR, broadcasted DSR, and PTR) using three rice varieties (BRRI dhan83, BRRI dhan85, and Binadhan-19) under three landscape positions (highland, medium highland, and lowland) in three distinct districts and agroecological zones of Bangladesh. We evaluated productivity, profitability, energy use efficiency (EUE), energy productivity (EP), GWP, and yield-scaled emissions of each of these tillage and crop establishment systems. Results: Our results showed that the DSR had a similar or slightly lower yield (2–8 %) than PTR, but with lower labor use (15–47 %), lower production cost (US$ ∼150 ha−1), and higher net profit. Drill-DSR yielded similar to PTR under highlands and medium highlands, but as 9–16 % lower when grown on lowlands. EUE and EP were 15–40 % higher in DSR than in PTR due to lower energy requirements. Higher energy use in PTR primarily resulted from extra energy required for nursery raising, transplanting, puddling, and irrigation. DSR was associated with lower GWP and yield-scaled emissions of 56 to 66 % compared to PTR. Conclusions: This study suggests that DSR can be a more environmentally sound, economically viable, and climate-smart production system, found more suitable for highland and medium-highland environments. However, for the widespread adoption of DSR in Bangladesh and South Asia as a whole, the nuiances of landscape position should be considered and appropriate technological, social, and policy-level interventions will be necessary
SPDC-HG: an accelerator of genomic hybrid breeding in maize
No full textIntegrating multiple modern breeding techniques in maize has always been challenging. This study aimed to address this issue by applying a flexible sparse partial diallel cross design composed of 945 maize hybrids derived from 266 inbred lines across different heterotic groups. The research integrated genome-wide association studies, genomic selection and genomic evaluation of parental inbred lines to accelerate the breeding process for developing single-cross hybrids. Significant associations were identified for 7-25 stable single nucleotide polymorphisms (SNPs) associated with the general combining abilities (GCAs) of nine yield-related traits. Using the maizeGDB and NCBI databases, 264 candidate genes were screened and functionally annotated based on significant SNPs detected by at least three statistical methods. The marker set developed from these GCA SNPs significantly improved the prediction accuracy of hybrids across all traits. The GCA estimates of the inbred lines involved in the top 100 and bottom 100 hybrids consistently ranked at the top and bottom, thereby confirming the accuracy of the predictions. Furthermore, the top 100 crosses selected using BayesB, GBLUP and LASSO showed a 105.4-108.6% increase in average ear weight compared to the bottom 100 crosses in field validation, demonstrating strong selection gains. Notably, amongst the top 100 hybrids, A017/A037 and A037/A169, each containing six superior genotypes were registered as Suyu 161 and Tongyu 1701, respectively, by the National Crop Variety Approval Committee in China. These results highlight the effectiveness of genomic selection and provide valuable insights for advancing genomic hybrid breeding in maize.1847-186
Effect of ALS and 4-HPPD inhibitor herbicides on maize lines
No full textNicosulfuron and topramezone are herbicides with different mechanisms of action, and are recommended for weed control in maize (Zea mays L.). The objective of this study was to evaluate and compare the effect of both herbicides, at increasing doses of 0, 1× and 3×, equivalent to 0, 60, and 180 g ai ha−1 for nicosulfuron, and 0, 33.6, and 100.8 g ai ha−1 for topramezone, on physiological and agronomic characteristics in 29 maize lines, including S2, S3 and S4, using an alpha-lattice incomplete block design. The cluster analysis divided our genotypes into two groups for both herbicides, based on their higher or lower fresh weight. The results showed a reduction in the SPAD index for both herbicides at 7 days after application, and nicosulfuron caused a reduction in the green matter weight of 33.4%. Similarly, nicosulfuron caused a delay and a reduction in its doses, after an initial increase, for all the agronomic variables, female flowering (FF), male flowering (MF), plant height (PH), ear height (EH), and grain weight (GW), in doses of 60 and 180 g ai ha−1, while topramezone only affected PH (1×–3×) and EH (3×). When comparing the applications of both herbicides on the maize genotypes, a difference in female and male flowering of 5.09 and 4.86 days, respectively was observed. A differential response and greater damage to nicosulfuron were observed in maize genotypes, with respect to topramezone applications
Wheat breeding with skim-sequencing for genomic selection: a comparison of marker platforms
No full textThe promise of predictive genomics-assisted breeding relies on efficient, affordable, and abundant molecular markers. The quantity and quality of markers have greatly expanded, yet plant breeding programs have struggled to fully harness this power mainly using array-based genotyping, targeted amplicon sequencing platforms, or reduced representation, sequence-based genotyping including genotyping-by-sequencing (GBS). Leveraging modern sequencing technology, commercial laboratory products, and open-source software, we demonstrate how ultra-low coverage (skim-seq, 0.05-0.10x) can be a viable marker platform. We genotyped 1,709 wheat lines with GBS, a mid-density DArTAG SNP panel (TaDArTAG vs. 2.0), and skim-seq (0.07x). All skim-seq variants were identified from the pooled skim-seq data and a reference genome without the aid of high-coverage samples. STITCH software was used for imputation followed by filtering to obtain 125,682 markers. Comparing STITCH imputed values to high coverage samples resulted in the correct imputation for more than 96% of the markers. Using phenotypic data, a 5-fold cross validation was implemented for each marker platform. No one marker system performed the best in all test cases, with GBS often resulting in the highest correlation between observed and predicted values. The skim-seq correlations were typically within 0.03 of GBS, suggesting skim-seq can be a viable marker strategy for genomic prediction. As technology and computational pipelines advances, skim-seq appears to be a promising method to bridge the gap between targeted genotyping and whole-genome sequencing. The skim-seq method is highly flexible and can be optimized to a variety of program needs, potentially allowing for wide adoption by the plant breeding community
The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset
No full textComputer vision is increasingly used in farmers' fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimeter ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although today's AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90 %. However, the precision for stems with 54 % was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies