Unveiling the heterosis pattern of modern maize breeding in Southwest China through population structure and genetic diversity analysis

Maize (Zea mays L.) is an important food crop throughout the world and is also one of the earliest crops to use heterosis. In this study, we evaluated the genetic diversity, population structure, and selective sweep of 100 elite inbred maize lines collected from the current breeding program in Sichuan province, Southwest China, using 5,261,175 high-quality single nucleotide polymorphisms (SNPs). We discovered an abundance of genetic diversities and classified them into four groups. By combining kinship relationships, these groups were further divided into Tropic-local A, Improved-tropic, Tropic-local B, and Improved-local. Genomic differentiation was assessed using Fst values (0.21-0.44) as well as genetic diversity (pi = 6.07 x 10-4 - 6.61 x 10-4). We generated 900 (90 x 10) hybrids using 90 and 10 inbred maize lines from 100 diverse maize germplasms. All hybrids were evaluated for 10 traits in three replicate tests across two locations. We found that the patterns of G1 x G3, G1 x G4, G2 x G3, and G3 x G4 exhibited significant heterosis in yield-related traits and have been used in commercial breeding. In addition, we also explored the relationship between 10 traits of hybrid offspring and the number of heterozygous SNP. Under most heterosis modes, the best linear unbiased estimation (BLUE) value of the trait was highly consistent with the trend of deleterious SNPs, but there was a deviation in the G1 x G3 mode. Taken together, the results provide insight into the utilization of the current maize germplasm in Sichuan province to improve hybrid breeding

Identifying chickpea (Cicer arietinum L.) genotypes rich in ascorbic acid as a source of drought tolerance

Drought stress induces a range of physiological changes in plants, including oxidative damage. Ascorbic acid (AsA), commonly known as vitamin C, is a vital non-enzymatic antioxidant capable of scavenging reactive oxygen species and modulating key physiological processes in crops under abiotic stresses like drought. Chickpea (Cicer arietinum L.), predominantly cultivated in drought-prone regions, offers an ideal model for studying drought tolerance. We explored the potential of AsA phenotyping to enhance drought tolerance in chickpea. Using an automated phenomics facility to monitor daily soil moisture levels, we developed a protocol to screen chickpea genotypes for endogenous AsA content. The results showed that AsA accumulation peaked at 30% field capacity (FC)-when measured between 11:30 am and 12:00 noon-coinciding with the maximum solar radiation (32 degrees C). Using this protocol, we screened 104 diverse chickpea genotypes and two control varieties for genetic variability in AsA accumulation under soil moisture depletion, identifying two groups of genotypes with differing AsA levels. Field trials over two consecutive years revealed that genotypes with higher AsA content, such as BDNG-2018-15 and PG-1201-20, exhibited enhanced drought tolerance and minimal reductions in yield compared to standard cultivars. These AsA-rich genotypes hold promise as valuable genetic resources for breeding programs aimed at improving drought tolerance in chickpea

Proxydetection of the impact distance of trees on crops: An indicator of the Land Equivalent Ratio

Faidherbia albida is known to affect the yield of various crops, typically in a pattern where the impact decreases with increasing distance from the tree. While several studies have investigated the spatial extent of this effect, limited research has explored how this distance varies across different crops or its relationship with crop yield and the Land Equivalent Ratio. In this study, we used a geostatistical approach combined with multispectral UAV (Unmanned Aerial Vehicle) imagery to address these gaps in understanding. The results showed that, in contrast to its tripling effect on millet yield, F. albida does not have a significant impact on groundnut pod yield, it only improves its haulm yield under its crown by about 50 %. The geostatistical analysis showed that F. albida affects the groundnut crop up to 9.8-m, compared to 18-m for millet. Yield upscaling from subplots to the whole plot was achieved with an error of 8 % for groundnut pod yield and 13 % for haulm yield. Groundnut’s partial Land Equivalent Ratio (LERcp) was 1.02 for pod yield and 1.05 for haulm yield, which was lower than the LERcp for millet. We concluded that the distance at which agroforestry trees influence crops is a reliable predictor of their effect on yield and Land Equivalent Ratio. This approach offers a promising tool for future agroforestry studies, potentially guiding crop management strategies in agroforestry systems

High-throughput phenotyping discovers new stable loci controlling senescence rate in bread wheat

Non-destructive time-series assessment of chlorophyll content in flag-leaf (FLC) accurately mimics the senescence rate and the identification of genetic loci associated with senescence provides valuable knowledge to improve yield stability under stressed environments. In this study, we employed both unmanned aerial vehicles (UAVs) equipped with red-green-blue (RGB) camera and ground-based SPAD-502 instrument to conduct temporal phenotyping of senescence. A total of 262 recombinant inbred lines derived from the cross of Zhongmai 578/ Jimai 22 were evaluated for senescence-related traits across three environments, spanning from heading to 35 d post-anthesis. The manual senescence rate (MSR) was quantified using the FLC and the active accumulated temperature, and UAV derived vegetation index were utilized to assess the stay-green rate (USG) facilitating the identification of senescent and stay-green lines. Results indicated that higher senescence rates significantly impacted grain yield, primarily by influencing thousand-kernel weight, and plant height. Quantitative trait loci (QTL) mapping for FLC, USG, and MSR using the 50K SNP array identified 38 stable loci associated with RGB-based vegetation indices and senescence-related traits: among which 19 loci related to senescence traits from UAV and FLC were consistently detected across at least two growth stages, with nine loci likely representing novel QTL. This study highlights the potential of UAV-based high-throughput phenotyping and phenology in identifying critical loci associated with senescence rates in wheat, validating the relationship between senescence rates and yield-related traits in wheat, offering valuable opportunities for gene discovery and significant applications in breeding programs.1168-117

Mapping grain crop sowing date in smallholder systems using optical imagery

Sowing date prediction using Earth observation data is challenging in smallholder systems due to small field sizes, heterogeneity in management practices, and a lack of reference data. This study aims to develop a generalizable algorithm that does not require any ground data for calibration to map sowing date using the Normalized Difference Vegetation Index (NDVI) from three optical datasets: MODIS, Harmonized Landsat and Sentinel (HLS), and Sentinel-2. We applied Savitzky-Golay (SG) and spline smoothing algorithms to each dataset and developed a derivative approach to identify the inflection point that represents the Start of Season (SoS), which was then converted to sowing date. We applied our methodology to map the sowing date of winter wheat in Bihar, India and spring-summer maize in the state of Mexico, Mexico. Overall, Sentinel-2 data led to the highest accuracies, but the performance of the smoothing algorithm differed across locations. In India, prediction models using SG achieved an R2 of 0.45 and a root mean square deviation (RMSD) of 11.44 days. In Mexico, prediction models using spline performed best, with an R2 of 0.19 and an RMSD of 4.24 weeks. The lower accuracy in Mexico was due to more complex cropping patterns as well as noise in the observed sowing date dataset. Our algorithm shows potential to identify SoS, and ultimately sowing date, at scale using Sentinel-2 imagery. However, challenges from low-quality validation datasets, small field sizes, cloud cover, and landscape complexity continue to pose challenges to predict sowing date using Earth observation data products

Yield from the shadows: beyond top layer photosynthesis to enhance crop productivity

Photosynthesis research in crops typically focuses on upper canopy layers, which is partly for convenience and partly for the sake of achieving stable datasets under high light conditions. This neglects significant contributions from light - limited portions of the canopy within the lower layers. This study aimed to provide an empirical quantification of the role of these hidden layers of wheat canopies in the context of canopy scale productivity. We demonstrate that light-saturated photosynthetic rates (Asat) in middle and bottom layers at key growth stages can be strong predictors of grain yield. Despite variability in architecture across layers, light interception remained similar and key associations between biomass accumulation and yield with Asat emerged. Yield showed positive associations with photosynthesis in all canopy layers but was stronger at the top layer during grain filling and at the bottom layer during booting. Whole canopy photosynthetic rates were influenced by top layer architecture, N availability in the middle and bottom layers and leaf angles at the bottom of the canopy. Our findings suggest that measurements within hidden layers are required, and that optimizing middle and bottom layer Asat during the vegetative period and top layer Asat during grain filling can boost food security

Chapter 8. What do we know about the future of crop pests and diseases in relation to food systems?

Crop pests and diseases (P&D) can cause substantial yield losses and pose a threat to global food security. Losses at a regional level can even exceed 40 percent for crops like maize and rice. Most studies show that a warmer climate creates a conducive, albeit spatially variable, environment for P&D spread. However, existing foresight research is largely biophysical in nature and focuses on individual pathosystems, examined mostly at the national level. As such, projections of the magnitude of economic impacts of changing patterns of P&D are missing. Global assessment of model-based historical and future P&D impacts on food systems remains constrained by the small number of available models that can estimate yield losses under contrasting climate and agroecological conditions. Efforts are needed to improve data accessibility, model versatility, and simulation platforms and to establish international observation and modeling networks. Artificial intelligence (AI) and related methods can assist in the development of robust and adaptable models to capture the impacts of P&D on food systems.45-4

Rural credit, food security, and resilience: An empirical evaluation from Kenya

In this paper, we examine the role of credit in enhancing rural households’ food security and resilience. In so doing, we consider resilience as a higher order capacity outcome, different from traditional development outcomes associated with households’ or individuals’ welfare. We evaluate the effectiveness of two types of agricultural production credit products, one a traditional credit and one that is linked to rainfall index insurance to protect borrowers against the adverse effects of drought. Based on a randomized controlled trial conducted in Machakos county, Kenya, we report both intent-to-treat effects as well as local average treatment effects to demonstrate the impacts of these credit products not only among borrowers, but the broader effects of expanding rural credit markets. We see generally low levels of food security resilience among our sampled households, but we find compelling evidence that credit and expanded credit markets more broadly had beneficial impacts on enhancing households’ food security and resilience. Despite the differences in the two credit products being evaluated, we do not find an appreciable difference in the effects of the two credit types, concluding that the expansion of affordable agricultural credit markets should be among the key policy tools for building resilience among rural smallholders.44 page

Understanding genetic diversity and population structure of CIMMYT and IITA early maturing provitamin A maize (Zea mays L.) inbred lines using phenotypic traits and SNP markers for variable tolerance to drought and heat stress

Understanding genetic relationships of maize inbred lines before integrating them into a breeding program is crucial for optimizing heterosis. In this study we assessed the genetic diversity and population structure of 188 early maturing provitamin A maize inbred lines sourced from CIMMYT and IITA using 3.3 K SNPs markers and phenotypic traits data. The results showed highly significant statistical variability (p < 0.000) for key phenotypic traits such as, anthesis date (AD), anthesis-silking interval (ASI), senescence (SEN), ears per plant (EPP) and grain yield (GYD). Inbred lines DS19753, TZMI1989, DS197-206 and DS197-338 had high grain yield under drought and heat stress conditions whilst CML 486, DS197-308, DS197-185, and DS197-318 performed better under non-drought and heat stress conditions. The genetic diversity analysis revealed moderate genetic diversity (GD) of 0.24, and a polymorphic information content (PIC) of 0.20. A minor allele frequency (MAF) of 18% suggested relatively low genetic variation amongst the inbred lines whilst observed heterozygosity (Ho) of 0.02 and Fixation Index (F) of 0.90 indicated fixation of most loci in the inbred lines. Delta K was highest at K = 2 suggesting presence of two distinct genetic sub-populations. Analysis of Molecular Variance indicated that the estimated variance amongst the two populations (0.001) was minimal whilst most of the genetic variance occurs within populations (0.499). The phenotypic diversity revealed supported by genotypic variations among the assessed genotypes suggests a possibility of successful selection of superior drought and heat tolerant provitamin A maize inbred lines that can be used for developing hybrids

Determination of rice accession status using infochemical and visual cues emitted to sustainably control Diopsis apicalis dalman

This study assessed the host plant selection behavior of female stalk-eyed flies (SEFs) or Diopsis apicalis, where a Y-tube olfactometer was used to compare SEF attraction to the odor of leaves from four rice varieties (ITA306, WAB56-104, CG14, and RAM55). Another step of the evaluation consisted of pairing leaf odors from two rice varieties. Also, potted plants of the tested varieties were displayed in a screened cage and submitted to female SEF selection. The results indicated that the odor produced by leaves from rice varieties CG14, WAB56-104, and ITA306 significantly attracted SEFs, at rates of 81%, 70%, and 97%, respectively, while SEF females were rarely attracted by the odor of leaves from the resistant rice variety RAM55, at a rate of 35%. The results suggested that the use of a Y-tube olfactometer was similar to the use of a screened cage. The resistance exhibited by rice variety CG14 against SEFs is related to an antibiosis interaction acting as bait, while that in RAM55 is an antixenosis one. Farmers can plant the traditional CG14 variety on the edge of rice fields to draw SEFs and poison their larvae. However, RAM55 can be inserted in an intercropping system to repel SEFs from laying eggs. The authors recommend CG14 and RAM55 as candidates for breeding to create resistant lines against SEF