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Assessment of fall armyworm tolerant maize hybrids for sustainable maize production in sub-Saharan Africa
No full textFall armyworm (Spodoptera frugiperda (J.E. Smith)) has become a significant pest to maize production, causing huge yield losses in sub-Saharan Africa. This study evaluated three non-Bt maize hybrids with tolerance to fall armyworm (FAW) along with a commercial hybrid check for yield and agronomic performance under natural FAW infestation and chemical control conditions in both on-station and on-farm trials. Significant differences were observed among the hybrids with the FAW tolerant hybrids showing reduced leaf and ear damage compared to the commercial hybrid. These hybrids also exhibited high grain yield performance, outyielding the commercial check by 197-252%. Mean grain yield under FAW infestation ranged from 6009.88 to 7117.30 kg ha-1 without chemical control, and even higher (8441.24 kg ha-1) under limited chemical control. Stepwise multiple regression analyses identified ear aspect, husk cover and ear damage as key traits accounting for 98% of the total variation in grain yield under FAW infestation. Participatory variety selection showed high farmer preference for these hybrids. The availability of these hybrids to farmers promises to enhance food security, reduce the environmental impact of insecticides, alleviate cost burdens on farmers, and increase household income
The effect of agronomic filters on arable plant communities: What weeds are we selecting for?
No full textFunctional diversity in arable plant communities affects their detriment, as different arable plants occupy different niche spaces which dictate their competitiveness to the crop. This functional diversity can be examined using Grime's CSR triangle; most common arable plants are thought to occupy a region of this triangle indicating low levels of stress tolerance, and preference for disturbance and abundant nutrition. Prior research has, however, only examined this with regard to specific management practices or cropping systems, rather than the ecological conditions they generate, the ‘agronomic filters’ applied. Using a dataset of all arable plant species in Sweden, we used multivariate statistics to determine the functional characteristics of problematic weeds, and how they differed from other plant species present in these communities. This was examined with regard to Grime's life strategy, perceived detriment, conservation status, and preference for agronomic filters relating to nutrition, disturbance, moisture, and light. Our results show that intense agronomic management constrains the niche and limits the function of the non-crop community, with stress-tolerators (S) being absent and, as theorised, competitive and pure ruderals (CR and R respectively) overrepresented. CR strategists favoured nutrition, light and disturbance, and were often considered problematic according to agronomic experts. R strategists generally showed less preference for nutrition, and were more often considered rare and non-weedy, probably due to their lesser competitiveness. These findings can be applied by modifying the agronomic filters favoured by problematic weeds. Specifically, more effective nutrient management would break the ‘agronomic trap’ of fertilisation benefitting dominant, competitive weeds. Increased grazing or mowing is also suggested to limit plant height in favour of less competitive species, and increased cropping diversity will also alter selection for agronomic filters depending on crop niche. Using these agronomic filters, we provide a theoretical guide to achieving ecological weed management in practice
The search for best fertilizer combination to increase wheat (Triticum aestivum L.) yield in north-western Ethiopia
No full textWheat is among the major cereals of wider importance in the mid-altitudes and highlands of Ethiopia. But wheat yield is quite lower than what could be achievable. To address this gap, Ethiopia introduced the use of blended fertilizer in 2015. However, the response of wheat to these new fertilizers is not known. In this study, we evaluated changes in wheat yield resulting from the application of blended fertilizers relative to common practice. The study also aimed at identifying the rate of blended fertilizer appropriate for wheat production and to evaluate how the inclusion of potassium (K), boron (B), and zinc (Zn) to the blend affects wheat yield. For this study, two groups of treatments were set using RCBD design in two locations over two seasons. While the first group composed of no fertilizer application and five levels of NPSZnB blended fertilizer, the second group contains an additional three different fertilizer rates; including modest rate of NPS compound fertilizer alone and NPS blended with K, B, and Zn. A 100 kg/ha urea was uniformly applied to all treatments, except for the control plots. The findings showed that the new blended fertilizer resulted in significantly lower wheat grain yield compared to the common practice. A closer look at the treatments containing the blended fertilizers showed that the highest wheat grain yield was produced with the application of 300 kg/ha NPSZnB at both Burie (3460 kg/ha) and Farta (2290 kg/ha) sites. Compared with the NPS compound fertilizer at a rate of 100 kg/ha, there was no significant wheat yield advantage following the inclusion of Zn and B in the blend fertilizer. However, the inclusion of K resulted in a substantial increase of wheat grain yield at Farta site, while it significantly reduced yield at Burie site. Though wheat responded to blend fertilizer, the move from application of DAP and urea to the blended fertilizers does not result in significant yield increase as it was intended. Regardless of the quality of grain produced by the application of micronutrients, the inclusion of Zn and B to the blends did not increase wheat grain yield either. However, site-specific application of K is required as wheat significantly responded to K at Farta, but the application negatively affected yield at Burie site
Assessing single-trait and multitrait genomic prediction model abilities including significant GWAS markers for fusarium head blight disease resistance in wheat (Triticum aestivum)
No full textDisease resistance traits are complex and quantitative in nature. Breeders regularly evaluate multiple important traits across diverse environments to employ them in genomics-assisted breeding. In this study, we evaluated the prospects of genomic prediction models by incorporating genome-wide association study (GWAS) results into single-trait and multitrait genomic prediction scenarios, using two distinct panels: the NMBU panel and the GRAMINOR panel. A standard genomic prediction model (Base) and the Base model with the addition of significant GWAS markers as fixed covariates (Base + GWAS) were tested on both panels. The predictive ability of models was measured in terms of prediction ability by using Pearson's correlation method. An improvement of 0.05% to as high as a two-fold improvement was observed in both the panels for single-trait and multitrait scenarios. In general, multitrait models outperformed single-trait models regardless of whether the GWAS markers were included. This study further concludes that multitrait-based genomic predictions are superior to single trait-based ones when the associated traits are used and are well correlated.335-34
Whole maize flour could enhance food and nutrition security in Malawi
No full textMaize is the staple cereal in Malawi, with a daily per capita consumption of 383 g (dry matter basis), primarily consumed in the form of nsima, a thick porridge. We combined a milling experiment with focus group discussions (FGDs) to provide insights into mass and nutrient losses during maize grain dehulling and maize flour consumption patterns in rural Malawi. Milling batches (30 kg) of four maize grain varieties were dehulled at three abrasive disk dehullers under controlled conditions. The impact of maize variety and dehuller design on mass and nutrient losses during dehulling was statistically significant (p < 0.05), with a mean mass loss of 28.1 ± 5.7%, and nutrient losses of 9.8 ± 1.9% for protein, 61.7 ± 2.0% for zinc, and 47.7 ± 3.6% for iron. Six FGDs conducted in rural areas of Lilongwe District revealed a preference for refined flour due to convenience and cultural norms, despite the nutritional benefits of whole grain flour, which was recognized for its ability to provide satiety, particularly during periods of maize scarcity. Participants also highlighted switching between flour types based on seasonal maize availability, social stigma associated with whole grain flour, and awareness of nutrient losses during dehulling. Given Malawi’s precarious food insecurity situation, transitioning from dehulled maize flour nsima to whole maize flour or less refined nsima, is imperative. Our study findings can have food and nutritional savings for other southern Africa countries where the dehulling is a common practice
Réseau Africain pour l’Amélioration des Cultures des Zones Sèches
No full textLe Réseau Africain pour l’Amélioration des Cultures des Zones Sèches (ADCIN), établi en août 2023, est un reseau collaboratif mis en place la suite d’une réunion de consultation tenue au Sénégal en février 2022 et d’une reunion des membres du réseau organisée au Ghana en janvier 2023. Il regroupe plus de 17 pays et plus de 200 chercheurs issus de diverses disciplines et organisations agricoles. Notre vision est de créer un réseau dynamique et durable pour l’amélioration des cultures des zones sèches en Afrique, en tirant parti des forces collectives de ses membres afin d’accélérer l’accès des agriculteurs à des variétés améliorées.2 page
A crop-specific and time-variant spatial framework for characterizing rainfed wheat production environments in Ethiopia
No full textContext: Characterizing crop production environments is essential for targeted interventions, resource allocation, scaling localized findings, and agricultural decision-making. However, existing methods lack the spatial and temporal rigor required to capture spatial and temporal variability in crop production environments. Objective: This study aimed to introduce a data-driven and dynamic spatial framework that integrates crop area mapping with the delineation of agro-ecological spatial units (ASUs) to characterize Ethiopia's rainfed wheat crop production environments. Methods: Annual rainfed wheat areas for the 2021 and 2022 Meher growing seasons were mapped using an ensemble machine-learning approach, leveraging time-series satellite images and environmental data. Dynamic ASUs were delineated using pixel- and object-based clustering methods, considering short-term changes (annual ASUs for 2021 and 2022) and longer-term trends (ASUs developed using data aggregated over the period 2016-2022). Clustering was based on key biophysical variables, including climatic, soil, topographic, and vegetation indices derived from satellite images that capture crop growth and development over space and time. Results and conclusions: The framework captured the spatial and temporal variability of wheat production environments, demonstrating its scalability across space and time. Rainfed wheat area mapping across two growing seasons revealed an expansion in rainfed wheat areas, highlighting the evolving nature of rainfed wheat cultivation in Ethiopia. The integration of rainfed wheat area mapping with dynamic ASU delineation identified five main production environments for wheat in Ethiopia, allowing to better target future research and development activities toward increasing wheat productivity in the country. Significance: The developed framework can facilitate agronomic assessments and inform the targeting of agricultural interventions, with potential applications that extend beyond this case study of rainfed wheat in Ethiopia
Artificial intelligence meets genomic selection: comparing deep learning and GBLUP across diverse plant datasets
No full textTo enhance the implementation of genomic selection (GS) in plant breeding, we conducted a comprehensive comparative analysis of deep learning (DL) models and genomic best linear unbiased predictor (GBLUP) methods across 14 real-world datasets derived from diverse plant breeding programs. We evaluated model performance by meticulously tuning hyperparameters specific to each dataset, aiming to maximize predictive accuracy and reliability. Our results demonstrated that DL models effectively captured complex, non-linear genetic patterns, frequently providing superior predictive performance compared to GBLUP, especially in smaller datasets. However, neither method consistently outperformed the other across all evaluated traits and scenarios. The analysis revealed that the success of DL models significantly depended on careful parameter optimization, reinforcing the importance of rigorous model tuning procedures. In the discussion, we emphasize the complementary nature of DL and GBLUP methods, highlighting that the choice between these models should be driven by the specific characteristics of the traits under study and the evaluation metrics prioritized in breeding programs. These insights contribute practical guidelines for selecting and optimizing genomic prediction models to achieve robust outcomes in plant breeding contexts
Commercial limes (calcium hydroxide) in corn tortilla production: Changes in pH, color, sensory characteristics, and shelf life
No full textLime, an essential component of the nixtamalization process, influences several aspects of tortilla quality. This study evaluated the effect of six commercial limes of different purities on the quality of tortillas made from white and blue corn. The Ca(OH)(2) and heavy metal content in lime, color, pH, and calcium content in tortillas, acceptability by attributes, and shelf life were determined. Limes with lower levels of Ca(OH)(2) fell below the standard specifications and exceeded the limits for heavy metals such as arsenic (6.6 and 7.3 mg kg(-1)) and lead (2.4 mg > 21.6, blue > 5.7), calcium content (white > 200, blue > 176 mg 100 g(-1)), and lower luminosity. Moreover, these limes imparted better organoleptic characteristics to the tortillas, which led to a higher preference among the panelists. The purity of lime is a key factor in improving the quality and safety of the tortilla; thus, countries that adopt the nixtamalization process using lime should pay particular attention to the purity of the lime used in their processes, as it modifies the characteristics of the final product. In addition, manufacturers of food-grade lime must guarantee high purity to obtain a safe, high-quality food product.173–18