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Maize and pearl millet production in hot semi-arid north eastern Namibia under conventional tillage and conservation agriculture practices
No full textThis article focuses on the results from experiments conducted to test and compare the effects of selected agricultural practices and principles on maize and pearl millet production of the major cropping systems in north-eastern regions of Namibia. Conventional Tillage (CT), Minimum Tillage (MT), Minimum Tillage with Mulch (MT-M), Minimum Tillage with Rotation (MT-R) and Minimum Tillage with Mulch and Rotation (MT-MR) were the primary treatments tested. Significant differences were observed on pearl millet grain in the first season (p=0.0496) and for maize grain in the second season (p=0.0206). For pearl millet, CT yielded the highest with 1783.0 kg ha-1and MT (1520.8 kg ha-1) had the lowest pearl millet grain yield at SE 240.35. For maize, CT-MR yielded the highest maize grain, 3852.3 kg ha-1, Standard Error of Mean 240.35. Results suggest that CA has the potential to increase or maintain maize production while noting projected declines in crop production of at least 50% or more through the influence of climate change according to Namibia’s Country Climate Smart Agriculture Programme (2015 – 2030).21-2
Farmers' preferences for the next generation of maize hybrids: application of product concept testing in Kenya and Uganda
No full textStep-change innovation in seed product design by public sector crop breeding has led to major contributions to global food security. The literature, however, provides few insights on how to identify forward-looking innovation opportunities. Inspired by discussions in the product innovation literature, this article describes our application of product concept testing in the context of hybrid maize in Uganda and Kenya. We identified the following eight maize seed product concepts based on interactions with seed companies, crop breeders, and farmers: 'Resilience', 'Drought escape', 'Food and fodder', 'Home use', 'Green maize', 'Livestock feed', 'Intercropping', and 'Family nutrition'. These were described and presented to 2400 farmers using videos, where each farmer saw three concept-presentation videos. Farmers were most likely to have selected the resilience (Kenya and Uganda), drought escape (Uganda), and intercropping (Kenya) concepts. Farmers showed mixed interest in other concepts, such as home use and food and fodder, suggesting that investments in product production and promotion would be required in addition to investments in breeding. These results provide new entry points for conversations among transdisciplinary teams at regional and national levels on the current and future opportunities for crop breeding to respond to farmers' requirements for new seed products.1–1
Trade-offs between early planting and yellow rust resistance in wheat: Insights from screening experiments in the Indo-Gangetic plain
No full textWheat crops (Triticum aestivum) that are conventionally planted may exhibit susceptibility to yellow rust (YR). However, the disease can be mitigated if the crops are planted earlier than the recommended planting time. A wheat screening experiment was carried out at the Borlaug Institute of South Asia located in Ludhiana, Punjab, India. The purpose of the study was to gain a deeper understanding of the adaptation patterns of early planted wheat. Early planting was found to be more advantageous for production potential, as well as phenology, stature, and physiological traits. In a separate experiment, each year, the same number of genotypes were screened for YR by artificially inoculating them with pathogen spores. The well-adapted genotypes for early establishment tend to possess a greater vulnerability to YR infection. Furthermore, the infection type score for the genotype selected for early planting showed a significantly greater proportion of S (susceptible) type reactions than for the genotypes adapted to early planting. Intriguingly, more R (resistant) and moderately resistant types of reactions were observed in early-adapted genotypes than in timely-adapted ones. Therefore, further concentrated research on YR screening is required to assess the possibility of breeding early sown wheat in the northwest part of the Indo-Gangetic region
Sustainability evaluation of contrasting milpa systems in the Yucatán Peninsula, Mexico
No full textThe milpa agroecosystem is an intercropping of maize, beans, squash and other crops, developed in Mesoamerica, and its adoption is widely variable across climates and regions. An example of particular interest is the Yucatan Peninsula in Mexico, which holds highly diverse milpas, drawing on ancestral Mayan knowledge. Traditional milpas have been described as sustainable resource management models, based on long rotations within a slash-and-burn cycle in forest areas. Nevertheless, due to modernization and intensification processes, new variants of the approach have appeared. The objective of this study was to evaluate the sustainability of three milpa systems (traditional, continuous, and mechanized) in four case studies across the Peninsula, with emphasis on food self-sufficiency, social inclusion and adoption of innovations promoted by a development project. The Framework for the Evaluation of Agroecosystems using Indicators (MESMIS, for its Spanish acronym) was used for its flexible, participatory approach. A common group of indicators was developed despite regional differences between study cases, with a high level of farmer participation throughout the iterative process. The results show lower crop yields in traditional systems, but with lower inputs costs and pesticide use. In contrast, continuous milpas had higher value in terms of crop diversity, food security, social inclusion, and innovation adoption. Mechanized milpas had lower weed control costs. Profitability of cash crops and the proportion of forest were high in all systems. Highly adopted innovations across milpa types and study cases included spatial crop arrangement and the use of residues as mulches. However, most innovations are not adapted to local conditions, and do not address climate change. Further, women and youth participation is low, especially in traditional systems.9233–925
Improving the productivity and income of smallholder sorghum farmers: The role of improved crop varieties in Nigeria
No full textAmong others, biotic and abiotic constraints associated with climate variability contribute to the low productivity of sorghum in Nigeria and other Sub-Saharan African countries. In this regard, improved sorghum varieties (ISVs) have been developed to address the constraints and boost the productivity of smallholder sorghum farmers. However, there is a scarcity of empirical studies on the adoption and impacts of ISVs. Using plot-level data from 3308 plots, we examine the drivers and impacts of the adoption of ISVs on the productivity and net income of sorghum farmers in Nigeria. To do so, we estimate an endogenous switching regression (ESR) model, which accounts for potential selection bias from observed and unobserved heterogeneity, and we perform some robustness checks. Our results show that the adoption rate of ISVs is about 25% in the study area. Among other factors, access to varietal information and distance to the seed market strongly explain the adoption of ISVs. The adoption of ISVs led to an increase in sorghum yield and net income by 13% and 17% respectively. Our results suggest that most smallholder sorghum farmers will not benefit from the productivity and income gains, given the relatively low adoption of ISVs. Overall, our findings imply that policymakers and development partners should increase investments in promoting the widespread adoption of ISVs through interventions, such as improved extension services and accessibility of seeds to deliver productivity gains to smallholder sorghum farmers
How do chat apps support the use of farming videos in agricultural extension: A case study from Bihar, India
No full textFarmers and extension workers increasingly use chat apps like WhatsApp to access and share information, including farming videos. Few empirical studies have critically examined the roles of these novel extension practices in agricultural innovation systems. We asked 294 extension workers in Bihar, eastern India, to circulate three wheat agronomy videos. Extension workers relied on WhatsApp to share these videos in 70% of surveyed cases (n = 131). Follow-up interviews revealed that WhatsApp enabled highly efficient video sharing with farmers extension workers already knew, given that WhatsApp was embedded like “breakfast tea” in some communities in rural Bihar. However, interviewed extension workers expressed concern that WhatsApp-shared videos facilitated limited social inclusivity, limited two-way discussion, and thereby limited localization of farming advice, feedback loops, and relationship building, at least in this context. Looking further, we anticipate these challenges with person-to-person chat apps in agricultural extension may also apply to emerging agricultural advisory chatbots powered by large language models. For researchers, our results imply that socio-technical theories, rather than transfer-of-technology theories, are required to anticipate and observe heterogeneous uses and impacts of digital extension tools. For practitioners, our results imply that chat apps can helpfully support, not replace, face-to-face extension practices. In the words of one interviewed extension worker, treat chat apps like “chutney”: a helpful complement and inadequate substitute for “rice and dal” conversations and field demonstrations
Balancing sensitivity and specificity enhances top and bottom ranking in genomic prediction of cultivars
No full textGenomic selection (GS) is a predictive methodology that is revolutionizing plant and animal breeding. However, the practical application of the GS methodology is challenging since a successful implementation requires a good identification of the best lines. For this reason, some approaches have been proposed to be able to select the top (or bottom) lines with more Precision. Despite the varying popularity of methods, with some being notably more efficient than others, this paper delves into the fundamentals of these techniques. We used five models/methods: (1) RC, known as the Bayesian Best Linear Unbiased Predictor (GBLUP); (2) R, which is like RC but uses a threshold; (3) RO, Regression Optimum, that leverages the RC model in its training process to fine-tune the threshold; (4) B, Threshold Bayesian Probit Binary model (TGBLUP) with a threshold of 0.5 to classify the cultivars as top or non-top; (5) BO is the TGBLUP but the threshold used is an optimal probability threshold that guarantees similar Sensitivity and Specificity. We also present a benchmark comparison of existing approaches for selecting the top (or bottom) performers, utilizing five real datasets for comprehensive analysis. For methods that necessitate a rigorous tuning process, we suggest a streamlined tuning approach that significantly decreases implementation time without notably compromising performance. Our analysis revealed that the regression optimal (RO) method outperformed other models across the five real datasets, achieving superior results in terms of the F1 score. Specifically, RO was more effective than models R, B, RC, and BO by 60.87, 42.37, 17.63, and 9.62%, respectively. When looking at the Kappa coefficient, the RO model was better than models B, BO, R, and RC by 37.46, 36.21, 52.18, and 3.95%, respectively. In terms of Sensitivity, the RO model outperformed models B, R, and RC by 145.74, 250.41, and 86.20, respectively. The second-best model was the model BO. It is important to point out that in the first stage, the BO and RO approaches train a classification and regression model, respectively, to classify the lines as the top (bottom) or not the top (not the bottom). However, both the BO and RO approaches optimize a threshold in the second stage to perform the classification of the lines that minimize the difference between the Sensitivity and Specificity. The BO and RO methods are superior for the selection of the top (or bottom) lines. For this reason, we encourage breeders to adopt these approaches to increase genetic gain in plant breeding programs
Detection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2
No full textFall Armyworm (FAW), Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae), poses a significant risk to global food and income security by attacking various crops, particularly maize. Early detection and management of FAW infestation are crucial for mitigating its impact on crop yields. This study investigated the effect of FAW infestation on the spectral signature of maize fields and classified infestation severity in Bangladesh using Sentinel-2 satellite imagery and Random Forest (RF) classification. Field observations on FAW infestation severity (none, moderate, and severe), collected by the Bangladesh Department of Agricultural Extension during 2019 and 2020, were used to train the RF classifier. Six thousand nine hundred ninety-eight observations were collected from 579 maize fields through weekly scouting. The Kruskal-Wallis test and Dunn’s post-hoc test were applied to identify the most significant spectral bands (P < 0.05) for detecting FAW incidence and severity across different maize growth stages. The results demonstrated that the spectral reflectance from Sentinel-2 bands varied significantly among different classes of FAW infestation, with noticeable differences observed during the early developmental stages of maize (vegetative growth stages 3 to 8). RF identified nine spectral bands and two spectral vegetation indices as important for FAW infestation discrimination. The RF classifier was evaluated using five-fold cross-validation, achieving an overall accuracy between 74 % and 84 %. The independent test set’s accuracy ranged from 72 % to 82 %. The mean multiclass AUC ranged from 0.83 to 0.95. Moreover, the results demonstrated the feasibility of detecting the severity of FAW infestation using temporal Sentinel-2 data and machine learning techniques. These findings underscore the potential of remote sensing and machine learning techniques for effectively monitoring and managing crop pests. The study provides valuable insights for classifying FAW infestation using high-resolution multitemporal data
