Early leaf spot (ELS) is one of the major biotic constraints of groundnut production in West and Central Africa. A study using 6 × 6 F2 full diallel populations from six parents (NAMA, B188, PC79-79, QH243C, TS32-1, and CN94C) was conducted to assess the mode of inheritance of ELS resistance traits. The F2 and parents were grown in a randomized complete block design with three replications. Data was collected on ELS disease severity, and an area under disease progress curve (AUDPC) was estimated. The results revealed that additive and non-additive gene actions were involved in the inheritance of the ELS resistance traits, but additive gene action was predominant. Significant reciprocal cross effect was observed, suggesting cytoplasmic effect on ELS resistance. Graphical analysis also revealed the predominance of additive gene action for ELS resistance. The results suggest that early generation selection should be effective for ELS resistance. Looking at the distribution of array points along with the regression line, parental lines NAMA, PC79-79, and B188 would be suitable as good donors in an ELS disease resistance breeding program.

The present experiment was conducted in Mali to study the genetic variability and correlation of early leaf spot (ELS) resistance parameters and agro-morphological traits in groundnut using two F3 populations from crosses QH243C X NAMA and TS32-1 X NAMA. Estimates of genotypic coefficient of variation and phenotypic coefficient of variation revealed high value for pod yield, kernel yield, and ELS score at 60 and 80 days after sowing for the cross QH243C X NAMA. Low-to-moderate GCV and PCV were obtained for the remaining traits for both crosses. High heritability values coupled with high genetic advance as percentage of mean recorded for ELS_II, defoliation percent, pod yield in cross QH243C X NAMA; shelling percent for the cross TS32-1 X NAMA and ELS_III; and plant height, kernel yield in both crosses, indicate the significant role of additive gene action for inheritance of these traits. Correlation analysis indicated that pod and kernel yield were significant and positively correlated with 100 kernel weight and shelling percent. For cross QH243C X NAMA, kernel yield showed significant positive correlation with all ELS resistance components but the correlation was not significant for the cross TS32-1 X NAMA. Positive and significant correlation was observed between ELS resistance components themselves, suggesting that these components could be controlled by a similar polygenic system. The findings suggest that early generation selection should be effective for days to first flowering, days to 50% flowering, plant height, pod yield, kernel yield, 100 kernel weight and early leaf spot resistance which recorded the highest value of heritability in the two crosses.

Background Drone technologies, also known as "unmanned aerial vehicles" (UAVs) or "unmanned aerial systems" (UAS), have a wide range of applications, including mapping, land-use planning, crop/infrastructure damage assessment, fertilizer spraying and precision crop protection. However, its use is still very limited in the agricultural sector in Africa in general and in Burkina Faso in particular. Results This study was initiated to explore the potential of UAVs for optimizing cotton yields through monitoring and phytosanitary efficiency. To this end, phytosanitary treatments and monitoring of vegetation status using multispectral drone imagery were carried out on cotton. The experimental sample consisted of four (04) cotton fields in four (04) locations. Phytosanitary treatments and aerial photography were respectively carried out using a DJI AGRAS T16 spray drone and a DJI Phantom 4 Multispectral drone. Phytosanitary treatment with DJI AGRAS T16 drone resulted in savings of 32.41% in treatment duration, 23.58% in water, 1% in pesticides and 10.5% in labor compared with manual spraying, but did not have a positive impact on boll yields due to jassid populations attack. The RGB and NDVI vegetation indices generated by photogrammetric processing of multispectral images acquired by drone enabled us to perceive phytosanitary state of cotton plants during their development cycle. Conclusion Information provided by these vegetation indices and by this study in general can be used to improve the efficiency of phytosanitary treatments against cotton pests and diseases.