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Management of maize-legume conservation agriculture systems rather than varietal choice fosters human nutrition in Malawi
No full textMalawi smallholder farmers are facing climate-induced challenges that have increased food and nutrition insecurity in the country, thus sustainable intensification practices has been widely recommended. The objective of this study was to assess the effects of cropping systems with improved varieties on total system productivity and nutrition under different environments. The study involved on-farm experiments in ten communities in Central and Southern Malawi, incrementally established from 2005/2006 to 2018/2019 cropping seasons. Each community had six demonstration plots with three main treatments: conventional ploughing (CP): sole maize grown on seasonally constructed ridges and furrows; no-tillage (NT): sole maize grown on retained ridges with minimum soil disturbance and residue retained; and Conservation agriculture (CA): maize intercropped either cowpea, pigeon pea or groundnut on retained ridges as in NT. Our results show that total system nutrition was higher in CA treatments than NT and CP. The yields of maize were at least 800 kg ha−1 higher in CA and NT than CP despite the variety that was grown. Legume yields were also higher under CA and NT than CP. High protein yield was observed in CA systems (at least 100 kg ha−1 higher than CP) where maize and legume intercrops were rotated with grain legumes. Our results show nutrients and energy gains in CA and NT systems that can be invested in practices that increases the resilience of smallholder farmers to climate change. Conservation agriculture and NT systems have more influence on productivity of smallholder farms, despite the genotypes used (hybrids or OPVs).1067–108
Maize Seed Tracker – A digital decision support tool to enable MLN-free commercial maize seed production
No full text2 page
Ms44-SPT: Unique genetic technology simplifies and improves hybrid maize seed production in sub-Saharan Africa
No full textHybrid maize seed production in Africa is dependent upon manual detasseling of the female parental lines, often resulting in plant damage that can lead to reduced seed yields on those detasseled lines. Additionally, incomplete detasseling can result in hybrid purity issues that can lead to production fields being rejected. A unique nuclear genetic male sterility seed production technology, referred to as Ms44-SPT, was developed to avoid hybrid seed loss and to improve the purity and quality of hybrid maize production. Hybrid seed yield reduction following detasseling can be attributed to leaf loss. Our analyses showed an average 2.9 leaves are lost during the detasseling process, resulting in a seed yield reduction of 14.0%. These findings suggest that deploying the Ms44-SPT technology would avoid this seed yield loss. By simplifying hybrid production and increasing seed yields, Ms44-SPT could help drive hybrid replacement, providing smallholder farmers with better access to improved hybrids
A marker weighting approach for enhancing within-family accuracy in genomic prediction
No full textGenomic selection is revolutionizing plant breeding. However, its practical implementation is still very challenging, since predicted values do not necessarily have high correspondence to the observed phenotypic values. When the goal is to predict within-family, it is not always possible to obtain reasonable accuracies, which is of paramount importance to improve the selection process. For this reason, in this research, we propose the Adversaria-Boruta (AB) method, which combines the virtues of the adversarial validation (AV) method and the Boruta feature selection method. The AB method operates primarily by minimizing the disparity between training and testing distributions. This is accomplished by reducing the weight assigned to markers that display the most significant differences between the training and testing sets. Therefore, the AB method built a weighted genomic relationship matrix that is implemented with the genomic best linear unbiased predictor (GBLUP) model. The proposed AB method is compared using 12 real data sets with the GBLUP model that uses a nonweighted genomic relationship matrix. Our results show that the proposed AB method outperforms the GBLUP by 8.6, 19.7, and 9.8% in terms of Pearson’s correlation, mean square error, and normalized root mean square error, respectively. Our results support that the proposed AB method is a useful tool to improve the prediction accuracy of a complete family, however, we encourage other investigators to evaluate the AB method to increase the empirical evidence of its potential
Ethiopian Crop Type 2020 (EthCT2020) dataset: Crop type data for environmental and agricultural remote sensing applications in complex Ethiopian smallholder wheat-based farming systems (Meher season 2020/21)
No full textCrop type observation is crucial for various environmental and agricultural remote sensing applications including land use and land cover mapping, crop growth monitoring, crop modelling, yield forecasting, disease surveillance, and climate modelling. Quality-controlled georeferenced crop type information is essential for calibrating and validating machine learning algorithms. However, publicly available field data is scarce, particularly in the highly dynamic smallholder farming systems of sub-Saharan Africa. For the 2020/21 main cropping season (Meher), the Ethiopian Crop Type 2020 (EthCT2020) dataset compiled from multiple sources provides 2,793 harmonized, quality-controlled, and georeferenced in-situ samples on annual crop types (7 crop groups; 22 crop classes) at smallholder field level across the complex and highly fragmented agricultural landscape of Ethiopia. The focus was on rainfed, wheat-based farming systems. A nationwide ground data collection campaign (GDCC; Source 1) was designed using a stratification approach based on wheat crop calendar information, and 1,263 in-situ data samples were collected in selected sampling regions. This in-situ data pool was enriched with 1,530 wheat samples extracted from a) the Wheat Rust Toolbox (WRTB; Source 2; 734 samples), a database for wheat disease surveillance data [1] and b) an inhouse farm household survey database (FHSD; Source 3; 796 samples). Obtained field data was labelled according to the Joint Experiment for Crop Assessment and Monitoring (JECAM) guidelines for cropland and crop type definition and field data collection [2] and the FAO Indicative Crop Classification [3]. The EthCT2020 dataset underwent extensive processing including data harmonization, mixed pixel assessment through visual interpretation using 5 m Planet satellite image composites, and quality-control using Sentinel-2 NDVI homogeneity analysis. The EthCT2020 dataset is unique in terms of crop diversity, pixel purity, and spatial accuracy while targeting a countrywide distribution. It is representative of Ethiopia's complex and highly fragmented agricultural landscape and can be useful for developing new machine learning algorithms for land use land cover mapping, crop type mapping, agricultural monitoring, and yield forecasting in smallholder cropping systems. The dataset can also serve as a baseline input parameter for crop models, climate models, and crop disease and pest forecasting systems
Mycotoxic effects of entomopathogenic fungi of fall armyworm (Spodoptera frugiperda J.E. Smith) on poultry feed safety
No full textThis study was carried out to analyze mycotoxins of entomopathogenic fungi of fall armyworm (Spodoptera frugiperda J.E. Smith) and poultry feed safety. An experiment was set up to assess the types of mycotoxins produced by entomopathogenic fungal parasite of fall armyworm larvae and their subsequent effect on the safety of the larvae as a feed ingredient. Molecular characterization was done to estimate the diversity of entomopathogenic fungi on fall armyworm larvae specimens from the treatment plots. Sequenced data was analyzed and processed using Molecular Evolutionary Genetic Analysis 6.0 software. The results showed relative diversity of fall armyworm larvae with 11 species isolated belonging to Aspergillus, Penicillium, Fusarium, Trichoderma, Bipolaris and Irpex genus. Some of these are potential mycotoxin producing fungi. Although isolated fungi potentially produce Ochratoxin, Fumonisin, Zearalenone and Trichothecene mycotoxins, only aflatoxins were analyzed in this study. About 3.98 mu g /kg of aflatoxin was observed using the ELISA total assay which is within the threshold toxicity levels set in Kenya for feed of about 20 mu g /kg. While this is under the threshold set by Kenya, it is still enough to cause concern as the cumulative exposure of even low doses can have impacts. This study therefore concludes that, Spodoptera frugiperda can be potentially contaminated with aflatoxins and when formulating poultry diets, there is need to monitor production so that the quality is not compromised and feed safety is ensured. Further studies are recommended to determine how much produced aflatoxins are then transferred into the poultry products such as eggs and meat
Graphical analysis of multi-environmental trials for bread wheat (Triticum aestivum L.) grain yield based on GGE Bi-Plot analysis
No full textBread wheat (Triticum aestivum L.) is a crucial crop in Ethiopia, and breeders test newly developed elite lines for superiority to existing cultivars to boost national productivity. The study was undertaken during the 2021–22 to 2022–23 cropping seasons at seven environments in optimum moisture areas of Ethiopian using 36 diverse and advanced bread wheat genotypes to evaluate the GEI by the graphical method of GGE biplot and to identify the genotypes with high mean yield performance and stability. Field experiments were conducted at the Adet, Asasa, Kulumsa, and Sinana research centers in Ethiopia. The experiments were planted in an alpha lattice design replicated three times in six rows of 2.5m long. Row-to-row distance and distance between blocks were 0.2m and 1.5m, respectively. The analysis of variance revealed that genotype, environment, and their interaction showed a highly significant effect on the yield as reflected in the GGE model and the GGE model indicated the suitability of the genotypes EBW202136 (33), Boru (1), and EBW202172 (12), with high mean yield and stability, whereas the genotypes EBW202185 (16) and Deka (36) produced high mean yield, but unstable. Likewise, the genotypes EBW202164 (27) and EBW202192 (29) produced low mean yield and unstable. The AMMI analysis of variance for grain yield across the environments showed that 17.26% of the total variation was attributed to genotypic effects, 64.03% to environmental effects, and 18.71% to GEI effects. Two mega environments were identified based on GGE biplot analysis and the which-won–model indicated the adaptation of genotypes Boru (1), EBW202159 (4), EBW202172 (12), EBW202171 (19), and EBW202136 (33) to first mega-environment and genotypes EBW202157 (3), EBW202166 (5), EBW202160 (6), EBW202162 (9), EBW202185 (16), Dursa (17) and Deka (36) in the second. These approaches allowed the identification of stable and high-yielding genotypes (EBW202136 (33) and EBW202172 (12)) which can be included in the national verification program, with a plan to release a new variety, and other genotypes with high yield could be utilized in breeding programs to further improve grain yield in bread wheat.150-16
Spatially explicit trends in nitrogen use efficiency and partial profits in Tanzania, 2008-2020
No full textAmidst concerns of global agricultural productivity growth slowdown, there is an emerging consensus that crop productivity growth in some African countries has either been stagnant or slow in the past decade. This slowdown is attributed to degrading soil health and volatile weather patterns resulting in low partial productivity especially of nitrogen—hereafter nitrogen use efficiency (NUE) and falling profits. We contribute to this literature by using plot level nationally representative panel data (2008-2020) for Tanzania to examine if indeed NUEs and profits have been on a downward spiral. We combine a causal random forest model for heterogeneous treatment effects and a regional market economic surplus model to explore the economic implications of the NUE trends. We find that NUEs differ not only across years, but also spatially. Additionally, the whole crop response curve differs across seasons and regions which poses enormous complexity when understanding NUEs using observational data implying that complex processes operate in low input farms that make it difficult to pin down the major challenges causing any slowdowns or increases in NUEs. We do not find any evidence of falling NUEs in Tanzania. Conversely, NUEs have increased as compared to 2008 (9 kg maize per kg of N) by about 10% in 2010, 18% in 2012, 18% in 2014 and 14% in 2020. Nonetheless, the profits and economic surpluses from nitrogen use are too low to incentivize farmers to use nitrogen fertilizers at the prevailing input and maize output price ratios. To address food insecurity concerns while incentivizing farmers to apply modest amounts of nitrogen fertilizer, this paper argues for increasing NUEs through proper crop management decisions and minimizing the fertilizer-maize grain price ratios.32 page