Phenological optimization of late reproductive phase for raising wheat yield potential in irrigated mega-environments

Increasing grain number through fine-tuning duration of the late reproductive phase (LRP; terminal spikelet to anthesis) without altering anthesis time has been proposed as a genetic strategy to increase yield potential (YP) of wheat. Here we conducted a modelling analysis to evaluate the potential of fine-tuning LRP in raising YP in irrigated mega-environments. Using the known optimal anthesis and sowing date of current elite benchmark genotypes, we applied a gene-based phenology model for long-term simulations of phenological stages and yield-related variables of all potential germplasm with the same duration to anthesis as the benchmark genotypes. These diverse genotypes had the same duration to anthesis but varying LRP duration. Lengthening LRP increased YP and harvest index by increasing grain number to some extent and an excessively long LRP reduced YP due to reduced time for canopy construction for high biomass production of pre-anthesis phase. The current elite genotypes could have their LRP extended for higher YP in most sites. Genotypes with a ratio of the duration of LRP to pre-anthesis phase of about 0.42 ensured high yields (≥95% of YP) with their optimal sowing and anthesis dates. Optimization of intermediate growth stages could be further evaluated in breeding programmes to improve YP.

Using a gene-based phenology model to identify optimal flowering periods of spring wheat in irrigated mega-environments

To maximize the grain yield of spring wheat, flowering needs to coincide with the optimal flowering period (OFP) by minimizing frost and heat stress on reproductive development. This global study conducted a comprehensive modelling analysis of genotype, environment, and management to identify the OFPs for sites in irrigated mega-environments of spring wheat where the crop matures during a period of increasing temperatures. We used a gene-based phenology model to conduct long-term simulation analysis with parameterized genotypes to identify OFPs and optimal sowing dates for sites in irrigated mega-environments, considering the impacts of frost and heat stress on yield. The validation results showed that the gene-based model accurately predicted wheat heading dates across global wheat environments. The long-term simulations indicated that frost and heat stress significantly advanced or delayed OFPs and shrank the durations of OFPs in irrigated mega-environments when compared with OFPs where the model excluded frost and heat stress impacts. The simulation results (incorporating frost and heat penalties on yield) also showed that earlier flowering generally resulted in higher yields, and early sowing dates and/or early flowering genotypes were suggested to achieve early flowering. These results provided an interpretation of the regulation of wheat flowering to the OFP by the selection of sowing date and cultivar to achieve higher yields in irrigated mega-environments.

Multienvironment Testing for Trait Stability and G × E Interaction on N2 Fixation, Plant Development, and Water-Use Efficiency of 21 Elite Groundnut (Arachis hypogaea L.) Genotypes in the Guinea Savanna

Groundnut production constitutes an integral part of the livelihoods of the people in the Guinea savanna of West Africa. This region accounts for over 70% of the total groundnut production in Ghana, 90% in Nigeria, and 100% in Mali and Burkina Faso. However, harsh environmental conditions often result in drastic yield reductions. In this study, we identified groundnut genotypes with superior symbiotic efficiency, greater pod yield, and plant water-use efficiency from 21 advanced groundnut breeding lines from ICRISAT after testing them at three locations in the Guinea savanna of Ghana over two consecutive years. Average N contribution by the groundnut genotypes ranged from 48 to 108 kg N ha^-1, and mean pod yield from 580 to 2,100 kg ha^-1. Genotype 17 (ICGV-IS 08837) produced about 2.5-fold more pods than genotype 1 (Chinese), which was the most widely cultivated variety by farmers. Of the 21 genotypes studied, genotype 16 (ICGV 99247) recorded the highest shoot δ¹³C value and was superior in water-use efficiency, which was consistent with stability estimates and mean performance. We also measured the effects of G × E on pod yield, N₂ fixation, shoot δ¹³C, and mega-environments for testing groundnut in the Guinea savanna, and these were all significant, although the effect was minimal on shoot δ¹³C values. Of the locations studied, Nyankpala and Damongo were more discriminating, and each constituted a mega-environment for conducting future groundnut trials in the Guinea savanna. Genotype 3 (ICG 6222) emerged as the best cultivar for the Damongo mega-environment, while genotype 17 was the best genotype for the Nyankpala mega-environment. The genotypes exhibiting the highest sensitivity of N₂ fixation in the environment included genotype 3 (ICG 6222), genotype 4 (ICGV 00068), and genotype 10 (ICGV 03315) (b_i > 1.3), while P_i estimates ranked genotypes 3, 10, and 17 as the best groundnut cultivars in terms of symbiotic N contribution. Based on the results of this study, genotype 17 (ICGV-IS 08837), genotype 3 (ICG 6222), genotype 10 (ICGV 03315), and genotype 4 (ICGV 00068), which were the most outstanding in terms of the overall pod yield, shoot biomass production, and amount of N-fixed, were the most suitable candidates to recommend for use in developing new varieties for the Guinea savanna of Ghana. Genotype 17 (ICGV-IS 08837) has already been released as a commercial variety for the Guinea savanna of Ghana since October 2018.

Multi-Site Bundling of Drought Tolerant Maize Varieties and Index Insurance

Drought Tolerant Maize Varieties (DTMV) and Rainfall Index Insurance (RII) are potential complements, though with limited empirical basis. We employ a multivariate spatial framework to investigate the potential for bundling DTMV with a simulated multi-site and multi-environment RII, designed to insure against mild, moderate and severe drought risk. We use yield data from on-farm trials conducted by the International Maize and Wheat Improvement Center (CIMMYT) and partners over 49 locations in Eastern and Southern Africa spanning 8 countries and 5 mega-environments (dry lowland, dry mid altitude, wet lower mid altitude, low wetland and wet upper mid altitude) in which 19 different improved maize varieties including DTMV were tested at each location. Spatially correlated daily rainfall data are generated from a first-order two-state Markov chain process and used to calibrate the index and predict yields with a hierarchical Bayes multivariate spatial model. Results show high variation in the performance and benefits of different bundles which depend on the maize variety, the risk layer insured, and the type of environment, with high chances of selecting a sub-optimal and unattractive contract. We find that complementing RII with a specific DTMV produces contracts with lower premiums and higher guaranteed returns especially in dry lowland increasing the chances of scaling up RII within this environment.