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Try before you buy: Experimental evidence on willingness to pay for tailored extension advice in Nigeria
No full textDespite the recent advances in the development of digital decision support tools (DSTs) for agriculture and their potential in the transition of extension systems from general to tailored services, there is limited evidence of their widespread adoption in developing countries. Relatedly, there is growing concern about the long-term sustainability of digital advisory approaches, especially where extension systems face resource constraints. Using a randomized controlled trial and a choice experiment in Nigeria, we examine farmers’ preferences and willingness-to-pay (WTP) for a targeted digital decision support system that includes various tailored advice. We assess whether farmers’ WTP is influenced by a two-year free provision of DST-mediated site-specific nutrient management advice combined with information on investment returns. We find that farmers are willing to pay varying amounts of subscription fees to access different tailored advice, with the largest WTP value for climate advisory services. Results show that two years of exposure to experimentation and learning through DST interventions significantly increase farmers’ WTP for tailored advice, in particular for soil fertility management and market information. Our findings suggest that a fee-based digital extension model is a promising demand-driven approach that can enhance the upscaling of DSTs and support the financial sustainability of extension service providers.47 page
Genetic progress in cowpea [Vigna unguiculata (L.) Walp.] stemming from breeding modernization efforts at the International Institute of Tropical Agriculture
No full textGenetic gain has been proposed as a quantifiable key performance indicator that can be used to monitor breeding programs' effectiveness. The cowpea breeding program at the International Institute of Tropical Agriculture (IITA) has developed and released improved varieties in 70 countries globally. To quantify the genetic changes to grain yield and related traits, we exploited IITA cowpea historical multi-environment trials (METs) advanced yield trial (AYT) data from 2010 to 2022. The genetic gain assessment targeted short duration (SD), medium duration (MD), and late duration (LD) breeding pipelines. A linear mixed model was used to calculate the best linear unbiased estimates (BLUE). Regressed BLUE of grain yield by year of genotype origin depicted realized genetic gain of 22.75 kg/ha/year (2.65%), 7.91 kg/ha/year (0.85%), and 22.82 kg/ha/year (2.51%) for SD, MD, and LD, respectively. No significant gain was realized in 100-seed weight (Hsdwt). We predicted, based on 2022 MET data, that recycling the best genotypes at AYT stage would result in grain yield gain of 37.28 kg/ha/year (SD), 28.00 kg/ha/year (MD), and 34.85 kg/ha/year (LD), and Hsdwt gain of 0.48 g/year (SD), 0.68 g/year (MD), and 0.55 g/year (LD). These results demonstrated a positive genetic gain trend for cowpea, indicating that a yield plateau has not yet been reached and that accelerated gain is expected with the recent integration of genomics in the breeding program. Advances in genomics include the development of the reference genome, genotyping platforms, quantitative trait loci mapping of key traits, and active implementation of molecular breeding
Enviromic prediction enables the characterization and mapping of Eucalyptus globulus Labill breeding zones
No full textGenotype-environment interaction is pervasive in forest genetics. Delineation of spatial breeding zones (BZs) is fundamental for accommodating genotype-environment interaction. Here we developed a BZ classification pipeline for the forest tree Eucalyptus globulus in 2 Australian regions based on phenotypic, genomic, and pedigree data, as well on a detailed environmental characterization ("envirotyping") and spatial mapping of BZs. First, the factor analytic method was used to model additive genetic variance and site-site genetic correlations (r(B)) in stem volume across 48 trials of 126,467 full-sib progeny from 2 separate breeding programs. Thirty-three trials were envirotyped using 145 environmental variables (EVs), involving soil and landscape (71), climate (73), and management (1) EVs. Next, sparse partial least squares-discriminant analysis was used to identify EVs that were required to predict classification of sites into 5 non-exclusive BZ classes based on r(B). Finally, these BZs were spatially mapped across the West Australian and "Green Triangle" commercial estates by enviromic prediction using EVs for 80 locations and 15 sets of observed climate data to represent temporal variation. The factor analytic model explained 85.9% of estimated additive variance. Our environmental classification system produced within-zone mean r(B) between 0.76 and 0.84, which improves upon the existing values of 0.62 for Western Australia and 0.67 for Green Triangle as regional BZs. The delineation of 5 BZ classes provides a powerful framework for increasing genetic gain by matching genotypes to current and predicted future environments
Genotype x environment interaction: trade-offs between the agronomic performance and stability of durum (Triticum turgidum) wheat to stem-rust resistance in Kenya
No full textStem rust significantly threatens durum wheat production, often resulting in substantial yield losses. To better understand resistance mechanisms and the stability of durum lines in stem rust-prone environments, this study evaluated 49 durum genotypes over three seasons at the Kenya Agricultural and Livestock Research Organization in Njoro. Utilizing 7 x 7 alpha lattice design, we assessed adult-plant resistance, monitored disease progression through final disease score (FDS) and area under the disease progress curve (AUDPC), and evaluated agronomic performance. Statistical analyses revealed significant seasonal and genotypic effects on FDS, AUDPC, spike length, and grain yield (p <= 0.01; p <= 0.001), with important genotype-by-season interactions (p <= 0.05; p <= 0.001). Broad-sense heritability for AUDPC was high at 0.91 and moderate at 0.35 for kernels per spike, underscoring the genetic basis of these traits. Notably, we observed negative correlations between disease parameters and agronomic traits, suggesting potential trade-offs. GGE biplot analysis singled out the first season (main season of 2019) as crucial for evaluating stem rust resistance and identified several durum lines, such as G45 and G48, as consistently resistant across all conditions. Furthermore, this analysis highlighted G45, G48, G176 and G189 as the highest yielding and most stable lines. The discovery of these resistant and high-performing genotypes is critical for enhancing durum breeding programs, helping to mitigate the impact of stem rust and improve yield stability
Bundling subsurface drip irrigation with no-till provides a window to integrate mung bean with intensive cereal systems for improving resource use efficiency
No full textThe future of South Asia’s major production system (rice–wheat rotation) is at stake due to continuously aggravating pressure on groundwater aquifers and other natural resources which will further intensify with climate change. Traditional practices, conventional tillage (CT) residue burning, and indiscriminate use of groundwater with flood irrigation are the major drivers of the non-sustainability of rice–wheat (RW) system in northwest (NW) India. For designing sustainable practices in intensive cereal systems, we conducted a study on bundled practices (zero tillage, residue mulch, precise irrigation, and mung bean integration) based on multi-indicator (system productivity, profitability, and efficiency of water, nitrogen, and energy) analysis in RW system. The study showed that bundling conservation agriculture (CA) practices with subsurface drip irrigation (SDI) saved ~70 and 45% (3-year mean) of irrigation water in rice and wheat, respectively, compared to farmers’ practice/CT practice (pooled data of Sc1 and Sc2; 1,035 and 318 mm ha−1). On a 3-year system basis, CA with SDI scenarios (mean of Sc5–Sc8) saved 35.4% irrigation water under RW systems compared to their respective CA with flood irrigation (FI) scenarios (mean of Sc3 and Sc4) during the investigation irrespective of residue management. CA with FI system increased the water productivity (WPi) and its use efficiency (WUE) by ~52 and 12.3% (3-year mean), whereas SDI improved by 221.2 and 39.2% compared to farmers practice (Sc1; 0.69 kg grain m−3 and 21.39 kg grain ha−1 cm−1), respectively. Based on the 3-year mean, CA with SDI (mean of Sc5–Sc8) recorded −2.5% rice yield, whereas wheat yield was +25% compared to farmers practice (Sc1; 5.44 and 3.79 Mg ha−1) and rice and wheat yield under CA with flood irrigation were increased by +7 and + 11%, compared to their respective CT practices. Mung bean integration in Sc7 and Sc8 contributed to ~26% in crop productivity and profitability compared to farmers’ practice (Sc1) as SDI facilitated advancing the sowing time by 1 week. On a system basis, CA with SDI improved energy use efficiency (EUE) by ~70% and partial factor productivity of N by 18.4% compared to CT practices. In the RW system of NW India, CA with SDI for precise water and N management proved to be a profitable solution to address the problems of groundwater, residue burning, sustainable intensification, and input (water and energy) use with the potential for replication in large areas in NW India
High-throughput phenotyping using hyperspectral indicators supports the genetic dissection of yield in durum wheat grown under heat and drought stress
No full textHigh-throughput phenotyping (HTP) provides new opportunities for efficiently dissecting the genetic basis of drought-adaptive traits, which is essential in current wheat breeding programs. The combined use of HTP and genome-wide association (GWAS) approaches has been useful in the assessment of complex traits such as yield, under field stress conditions including heat and drought. The aim of this study was to identify molecular markers associated with yield (YLD) in elite durum wheat that could be explained using hyperspectral indices (HSIs) under drought field conditions in Mediterranean environments in Southern Spain. The HSIs were obtained from hyperspectral imagery collected during the pre-anthesis and anthesis crop stages using an airborne platform. A panel of 536 durum wheat lines were genotyped by sequencing (GBS, DArTseq) to determine population structure, revealing a lack of genetic structure in the breeding germplasm. The material was phenotyped for YLD and 19 HSIs for six growing seasons under drought field conditions at two locations in Andalusia, in southern Spain. GWAS analysis identified 740 significant marker-trait associations (MTAs) across all the durum wheat chromosomes, several of which were common for YLD and the HSIs, and can potentially be integrated into breeding programs. Candidate gene (CG) analysis uncovered genes related to important plant processes such as photosynthesis, regulatory biological processes, and plant abiotic stress tolerance. These results are novel in that they combine high-resolution hyperspectral imaging at the field scale with GWAS analysis in wheat. They also support the use of HSIs as useful tools for identifying chromosomal regions related to the heat and drought stress response in wheat, and pave the way for the integration of field HTP in wheat breeding programs
Upgrading the post-harvest process of ISRA breeding programs
No full textISRA's post-harvest breeding programs processing capacity has been improved and modernized through its collaboration with CIMMYT.1 graphi
ACME (Agile Crop Model Ensemble): A package to generate and run large virtual experiments with ensembles of crop simulation models
No full textThe objective of the ACME (Agile Crop Model Ensemble) software is to facilitate the simulation of large virtual experiments using an ensemble of crop models on personal computers running Windows OS. These simulations can either be point-based or spatially explicit. The package includes an ensemble of three crop models (executable files and input and output data) and two databases: ‘MasterInputOutput’ which contains the input and output variables of the models, and ‘ModelDictionary’, which contains the connections and required transformations between variables of the different models. In addition, an executable code ‘DataMill’ applies these transformations and manages the data flow between the MasterInputOutput database and the file system of each model, before (input parameters) and after (output parameters) running the virtual experiment