Peanuts are a valuable source of nutrients, but peanut consumption patterns, consumption frequencies, and the factors influencing peanut consumption in Malawi are not known. This study surveyed consumers to fill this knowledge gap and to assess Malawian consumers’ readiness to try new food products. Out of the 489 respondents surveyed, all but three consumed peanuts (in any form). The majority (70.4%) consumed peanuts at least three times in a week. Chi-square test showed that demographic and socioeconomic variables had significant effects (p < 0.05) on peanut product preferences, the frequency of peanut consumption, and readiness to try new foods. For instance, women mostly preferred peanut flour compared to men, and peanut butter was the most preferred form for younger consumers. Logistic regression analysis showed that consumers with high school education or below were 2.35 times more likely to eat peanuts more often than consumers with post high school education. Among the participants that were ready to try new foods (54%), men and those with post high school education were 1.90 and 2.74 times more likely to try new foods than their respective counterparts. In general, the diversity of peanut products on the Malawian market is limited, and socioeconomic restrictions override consumer preferences. Therefore, future peanut-based food products innovations should explore ways to overcome such restrictions.

In this paper, we present a procedure for implementing field-based high-throughput plant phenotyping (HTPP) that can be used in resource-constrained research programs. The procedure relies on opensource tools with the only expensive item being one-off purchase of a drone. It includes acquiring images of the field of interest, stitching the images to get the entire field in one image, calculating and extracting the vegetation indices of the individual plots, and analyzing the extracted indices according to the experimental design. Two populations of groundnut genotypes with different maturities were evaluated for their reaction to early and late leaf spot (ELS, LLS) diseases under field conditions in 2020 and 2021. Each population was made up of 12 genotypes in 2020 and 18 genotypes in 2021. Evaluation of the genotypes was done in four locations in each year. We observed a strong correlation between the vegetation indices and the area under the disease progress curve (AUDPC) for ELS and LLS. However, the strength and direction of the correlation depended upon the time of disease onset, level of tolerance among the genotypes and the physiological traits the vegetation indices were associated with. In 2020, when the disease was observed to have set in late in medium duration population, at the beginning of the seed stage (R5), normalized green-red difference index (NGRDI) and variable atmospheric resistance index (VARI) derived at the beginning pod stage (R3) had a positive relationship with the AUDPC for ELS, and LLS. On the other hand, NGRDI and VARI derived from images taken at R5, and physiological maturity (R7) had negative relationships with AUDPC for ELS, and LLS. In 2021, when the disease was observed to have set in early (at R3) also in medium duration population, a negative relationship was observed between NGRDI and VARI and AUDPC for ELS and LLS, respectively. We found consistently negative relationships of NGRDI and VARI with AUDPC for ELS and LLS, respectively, within the short duration population in both years. Canopy cover (CaC), green area (GA), and greener area (GGA) only showed negative relationships with AUDPC for ELS and LLS when the disease caused yellowing and defoliation. The rankings of some genotypes changed for NGRDI, VARI, CaC, GA, GGA, and crop senescence index (CSI) when lesions caused by the infections of ELS and LLS became severe, although that did not affect groupings of genotypes when analyzed with principal component analysis. Notwithstanding, genotypes that consistently performed well across various reproductive stages with respect to the vegetation indices constituted the top performers when ELS, LLS, haulm, and pod yields were jointly considered.

Late leaf spot (LLS) is an important disease of peanut, causing global yield losses. Developing resistant varieties through breeding is crucial for yield stability, especially for smallholder farmers. However, traditional phenotyping methods used for resistance selection are laborious and subjective. Remote sensing offers an accurate, objective, and efficient alternative for phenotyping for resistance. The objectives of this study were to compare between regression and classification for breeding, and to identify the best models and indices to be used for selection. We evaluated 223 genotypes in three environments: Serere in 2020, and Nakabango and Nyankpala in 2021. Phenotypic data were collected using visual scores and two handheld sensors: a red–green–blue (RGB) camera and GreenSeeker. RGB indices derived from the images, along with the normalized difference vegetation index (NDVI), were used to model LLS resistance using statistical and machine learning methods. Both regression and classification methods were also evaluated for selection. Random Forest (RF), the artificial neural network (ANN), and k-nearest neighbors (KNNs) were the top-performing algorithms for both regression and classification. The ANN (R²: 0.81, RMSE: 22%) was the best regression algorithm, while the RF was the best classification algorithm for both binary (90%) and multiclass (78% and 73% accuracy) classification. The classification accuracy of the models decreased with the increase in classification classes. NDVI, crop senescence index (CSI), hue, and greenness index were strongly associated with LLS and useful for selection. Our study demonstrates that the integration of remote sensing and machine learning can enhance selection for LLS-resistant genotypes, aiding plant breeders in managing large populations effectively.

The economic return for peanut (Arachis hypogaea L.) in Ghana is often low due to limitations in the availability of inputs or their adoption, which are needed to optimize yield. Six experiments were conducted in Ghana in 2020 and 2021 to determine the impact of planting date, cultivar, fertilization, pest management practices, and harvest date on peanut yield, financial return, and pest reaction. A wide range of interactions among these treatment factors were often observed for infestations of aphids (Aphis gossypii Glover); groundnut rosette disease (Umbravirus: Tombusviridaee); millipedes (Peridontopyge spp.); white grubs (Schyzonicha spp.); wireworms (Conoderus spp.); termites (Microtermes and Odontotermes spp.); canopy defoliation as a result of early leaf spot disease caused by Passalora arachidicola (Hori) and late leaf spot caused by Nothopassalora personata (Berk. and M. A. Curtis); and the scarification and boring of pods caused by arthropod feeding. Pod yield and economic return increased for the cultivar Chitaochi and Sarinut 2 when fertilizer was applied and when fertilizer was applied at early, mid-, and late planting dates. Pod yield and economic return increased when a combination of locally derived potassium soaps was used for aphid suppression and one additional hand weeding was used in the improved pest management practice compared with the traditional practice without these inputs. Pearson correlations for yield and economic return were negatively correlated for all pests and damage caused by pests. The results from these experiments can be used by farmers and their advisors to develop production packages for peanut production in Ghana.

A chickpea simple sequence repeat (SSR) marker reference kit has been developed based on the genotyping of the global chickpea composite collection (3,000 accessions) with 35 SSR markers. The kit consists of three pools of chickpea accessions along with supporting documentation on the SSR markers, polymerase chain reaction and detection conditions, and the expected allele sizes for each of the 35 SSR loci. These markers were selected based on quality criteria, genome coverage and locus-specific information content. Other important SSR selection criteria were quality of amplification products, locus complexity, polymorphism information content and well-dispersed location on a chickpea genetic map. The developed SSR kit has a wide range of applications, especially for genetic diversity studies in chickpea. Using the markers and reference accessions in the kit, scientists in other laboratories will be able to compare the genotypic data that they obtain for their germplasm with that obtained using the global composite collection.

New technologies combined with improved genetics and farmer access are important components required to improve productivity and efficiencies of cropping systems. The ability of the public and private sector to provide these components to farmers often vary considerably and can be challenging because of limited resource allocation and investment in institutions designed to provide these services. Partnerships among national programs where resources are limited and external entities can provide an effective platform to deliver improved cultivars and production and pest management practices that increase crop yield and economic viability of resource-poor farmers. In this note, we describe a partnership between the Council for Scientific and Industrial Research-Crops Research Institute (CSIR-CRI) in Ghana, the International Center for Research in the Semi-Arid Tropics (ICRISAT), the US Agency for International Development Peanut Collaborative Research Support Program (USAID Peanut CRSP), the Feed the Future Innovation Lab on Peanut Productivity and Mycotoxin Control (PMIL), and North Carolina State University (NCSU) that resulted in the release of two ICRISAT-derived lines as cultivars to farmers in Ghana. The cultivars Otuhia (Arachis hypogaea L.) and Yenyawoso (Arachis hypogaea L.) were released by CSIR-CRI in 2012 following evaluations of breeding lines beginning in 1999. This case study provides insight into the research focus and timeline that occurred with this partnership during the research and development process. A portion of the data obtained to support release of these cultivars is provided.