Exploring the use of Sentinel-2 datasets and environmental variables to model wheat crop yield in smallholder arid and semi-arid farming systems.

Low levels of agricultural productivity are associated with the persistence of food insecurity, poverty, and other socio-economic stresses. Mapping and monitoring agricultural dynamics and production in real-time at high spatial resolution are essential for ensuring food security and shaping policy interventions. However, an accurate yield estimation might be challenging in some arid and semi-arid regions since input datasets are generally scarce, and access is restricted due to security challenges. This work examines how well Sentinel-2 satellite sensor-derived data, topographic and climatic variables, can be used as covariates to accurately model and predict wheat crop yield at the farm level using statistical models in low data settings of arid and semi-arid regions, using Sulaimani governorate in Iraq as an example. We developed a covariate selection procedure that assessed the correlations between the covariates and their relationships with wheat crop yield. Potential non-linear relationships were investigated in the latter case using regression splines. In the absence of substantial non-linear relationships between the covariates and crop yield, and residual spatial autocorrelation, we fitted a Bayesian multiple linear regression model to model and predict crop yield at 10 m resolution. Out of the covariates tested, our results showed significant relationships between crop yield and mean cumulative NDVI during the growing season, mean elevation, mean end of the season, mean maximum temperature and mean the start of the season at the farm level. For in-sample prediction, we estimated an R2 value of 51 % for the model, whereas for out-of-sample prediction, this was 41 %, both of which indicate reasonable predictive performance. The calculated root-mean-square error for out-of-sample prediction was 69.80, which is less than the standard deviation of 89.23 for crop yield, further showing that the model performed well by reducing prediction variability. Besides crop yield estimates, the model produced uncertainty metrics at 10 m resolution. Overall, this study showed that Sentinel-2 data can be valuable for upscaling field measurement of crop yield in arid and semi-arid regions. In addition, the environmental covariates can strengthen the model predictive power. The method may be applicable in other areas with similar environments, particularly in conflict zones, to increase the availability of agricultural statistics.

Evaluation of gene flow and genetic diversity in rice accessions across Kurdistan region-iraq using SSR markers.

BACKGROUND: In recent years, farmers have complained that the only way to obtain seeds is to select plants that show good performance under local climate conditions in the region. This study aimed to investigate the diversity of rice accessions grown in the region to build a breeding program. METHODS AND RESULTS: A total of 62 accessions of rice from farmers and research stations were collected from the Kurdistan region, including short-grain and long-grain types, for molecular genetics and diversity analysis. In this study, 37 polymorphic simple sequence repeat (SSR) markers were selected with several molecular genetics software programs. The results show that these SSR markers are very effective for this investigation, generating a total of 152 observed alleles (Na), 75.166 Effective number of alleles (Ne) and an average of 4.1 and 2.03 alleles per locus, respectively. The average polymorphic information content (PIC) per locus was recorded as 0.404. The research presented here confirms two subpopulations, japonica (C1 and C2) and indica (C3), based on molecular genetics data analysis. Analysis of molecular variance revealed that the 72% variance was due to the variation among populations and 28% within the population. CONCLUSIONS: Altogether, these results indicate that there is very low gene flow. These results show the importance of the study of genetic diversity and relationships for starting breeding and improvement programs for rice in the Kurdistan region.

Illegitimacy and sibship assignments in oil palm (Elaeis guineensis Jacq.) half-sib families using single locus DNA microsatellite markers.

Oil palm breeding has been progressing very well in Southeast Asia, especially in Malaysia and Indonesia. Despite this progress, there are still problems due to the difficulty of controlled crossing in oil palm. Contaminated/illegitimate progeny has appeared in some breeding programs; late and failure of detection by the traditional method causes a waste of time and labor. The use of molecular markers improves the integrity of breeding programs in perennial crops such as oil palm. Four half-sib families with a total of 200 progeny were used in this study. Thirty polymorphic single locus DNA microsatellites markers were typed to identify the illegitimate individuals and to obtain the correct parental and progeny assignments by using the CERVUS and COLONY programs. Three illegitimate palms (1.5%) were found, and 16 loci proved to be sufficient for sibship assignments without parental genotypes by using the COLONY program. The pairwise-likelihood score (PLS) method was better for half-sib family assignments than the full likelihood (FL) method.