The rhizomicrobiome is composed of microbes that live in association with plant roots. From nutrient cycling to carbon sequestration, soil microorganisms have provided a solid base for natural and agricultural ecosystems to function. The relationship between plant roots and soil microorganisms is especially relevant in food staples such as rice (Oryza sativa L.), as the various properties of these microbes can influence crop yield and plant health, thereby affecting a major portion of the food supply for an ever-growing world population. In this study, we used 16S rRNA gene-based metagenomic analysis to investigate the impact of crop rotation and soil cultivation methods (no-till or tillage) on rhizosphere bacterial diversity and composition in eight crop fields in Arkansas. Illumina MiSeq sequencing revealed 56 Phyla, with four major Phyla: Proteobacteria, Acidobacteria, Actinobacteria, and Bacteroidetes. Soil microbial communities in the samples studied were phylogenetically diverse but with a stable community structure. Crop rotation and tillage did not significantly affect bacterial diversity.
The production of onions bulbs (Allium cepa L.) requires a high amount of nitrogen. According to the demand of sustainable agriculture, the information-development and communication technologies allow for improving the efficiency of nitrogen fertilization. In the south of the province of Buenos Aires, Argentina, between 8000 and 10,000 hectares per year−1 are cultivated in the districts of Villarino and Patagones. This work aimed to analyze the relationship of biophysical variables: leaf area index (LAI), canopy chlorophyll content (CCC), and canopy cover factor (fCOVER), with the nitrogen fertilization of an intermediate cycle onion crop and its effects on yield. A field trial study with different doses of granulated urea and granulated urea was carried out, where biophysical characteristics were evaluated in the field and in Sentinel-2 satellite observations. Field data correlated well with satellite data, with an R2 of 0.91, 0.96, and 0.85 for LAI, fCOVER, and CCC, respectively. The application of nitrogen in all its doses produced significantly higher yields than the control. The LAI and CCC variables had a positive correlation with yield in the months of November and December. A significant difference was observed between U250 (62 Mg ha−1) and the other treatments. The U500 dose led to a yield increase of 27% compared to U250, while the difference between U750 and U500 was 6%.
Image processing entered the era of artificial intelligence, and machine learning algorithms emerged as attractive alternatives for time series data processing. Satellite image time series processing enables crop phenology monitoring, such as the calculation of start and end of season. Among the promising algorithms, Gaussian process regression (GPR) proved to be a competitive time series gap-filling algorithm with the advantage of, as developed within a Bayesian framework, providing associated uncertainty estimates. Nevertheless, the processing of time series images becomes computationally inefficient in its standard per-pixel usage, mainly for GPR training rather than the fitting step. To mitigate this computational burden, we propose to substitute the per-pixel optimization step with the creation of a cropland-based precalculations for the GPR hyperparameters θ . To demonstrate our approach hardly affects the accuracy in fitting, we used Sentinel-2 LAI time series over an agricultural region in Castile and Leon, North-West Spain. The performance of image reconstructions were compared against the standard per-pixel GPR time series processing. Results showed that accuracies were on the same order (RMSE 0.1767 vs. 0.1564 [m2/m2], 12% RMSE degradation) whereas processing time accelerated about 90 times. We further evaluated the alternative option of using the same hyperparameters for all the pixels within the complete scene. It led to similar overall accuracies over crop areas and computational performance. Crop phenology indicators were also calculated for the three different approaches and compared. Results showed analogous crop temporal patterns, with differences in start and end of growing season of no more than five days. To the benefit of crop monitoring applications, all the gap-filling and phenology indicators retrieval techniques have been implemented into the freely downloadable GUI toolbox DATimeS.
The endophytic fungi Serendipita indica and S. vermifera have recently gained increasing attention due to their beneficial effects on plant growth and plant health. Little is known about other species, such as S. williamsii and S. herbamans. To test their biocontrol and growth-promoting potential, susceptible and tolerant tomato cultivars (Kremser Perle and Micro-Tom, respectively) were inoculated with S. williamsii, S. herbamans, S. indica, or S. vermifera and challenged with the soilborne pathogen Fusarium oxysporum f. sp. lycopersici (Fol) in greenhouse experiments. Furthermore, in vitro assays on the direct inhibitory effects of Serendipita spp. against Fol were performed. Negative effects of Fol on phenological growth in the susceptible cultivar were alleviated by all four applied Serendipita spp. Apart from these similar effects on biometric parameters, disease incidence was only reduced by S. herbamans and S. vermifera. In the tolerant cultivar, disease parameters remained unaffected although shoot dry mass was negatively affected by S. vermifera. Direct effects of Serendipita spp. against Fol were not evident in the in vitro assays indicating an indirect effect via the host plant. Our results highlight the importance of identifying cultivar-specific effects in pathogen-endophyte-plant interactions to determine the most beneficial combinations.
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.
