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The accuracy of dryland crop classification using satellite-based synthetic aperture radar (SAR) data is often unsatisfactory owing to the similar dielectric properties that exist between the crops and their surroundings. The main objective of this study was to improve the accuracy of dryland crop (maize and cotton) classification by combining multitype features and multitemporal polarimetric SAR (PolSAR) images in Hebei plain, China. Three quad-polarimetric RADARSAT-2 scenes were acquired between July and September 2018, from which 117 features were extracted using the Cloude–Pottier, Freeman–Durden, Yamaguchi, and multiple-component polarization decomposition methods, together with two polarization matrices (i.e., the coherency matrix and the covariance matrix). Random forest (RF) and support vector machine (SVM) algorithms were used for classification of dryland crops and other land-cover types in this study. The accuracy of dryland crop classification using various single features and their combinations was compared for different imagery acquisition dates, and the performance of the two algorithms was evaluated quantitatively. The importance of all investigated features was assessed using the RF algorithm to optimize the features used and the imagery acquisition date for dryland crop classification. Results showed that the accuracy of dryland crop classification increases with evolution of the phenological period. In comparison with SVM, the RF algorithm showed better performance for dryland crop classification when using full polarimetric RADARSAT-2 data. Dryland crop classification accuracy was not improved substantially when using only backscattering intensity features or polarization decomposition parameters extracted from a single-date image. Satisfactory classification accuracy was achieved using 11 optimized features (derived from the Cloude–Pottier decomposition and the coherency matrix) from 2 RADARSAT-2 images (acquisition dates corresponding to the middle and late stages of dryland crop growth). This study provides an important reference for timely and accurate classification of dryland crop in Hebei plain, China.

Sustainable food production in the context of climate change necessitates diversification of agriculture and a more efficient utilization of plant genetic resources. Fonio millet ( Digitaria exilis ) is an orphan African cereal crop with a great potential for dryland agriculture. Here, we establish high-quality genomic resources to facilitate fonio improvement through molecular breeding. These include a chromosome-scale reference assembly and deep re-sequencing of 183 cultivated and wild Digitaria accessions, enabling insights into genetic diversity, population structure, and domestication. Fonio diversity is shaped by climatic, geographic, and ethnolinguistic factors. Two genes associated with seed size and shattering showed signatures of selection. Most known domestication genes from other cereal models however have not experienced strong selection in fonio, providing direct targets to rapidly improve this crop for agriculture in hot and dry environments. Fonio millet is a fast growing orphan cereal crop with a great potential for dryland agriculture. Here, the authors report chromosome-scale reference genome assembly and population genomic resources to shed light on genetic diversity, population structure and domestication of fonio millet.

Foxtail millet (Setaria italica) and Common millet (Panicum miliaceum) are the oldest domesticated dry farming crops in Eurasia. Identifying these two millets in the archaeobotanical remains are still problematic, especially because the millet grains preserve only when charred. Phytoliths analysis provides a viable method for identifying this important crop. However, to date, the identification of millet phytoliths has been questionable, because very little study has been done on their morphometry and taxonomy. Particularly, no clear diagnostic feature has been used to distinguish between Foxtail millet and Common millet. Here we examined the anatomy and silicon structure patterns in the glumes, lemmas, and paleas from the inflorescence bracts in 27 modern plants of Foxtail millet, Common millet, and closely related grasses, using light microscopy with phase-contrast and microscopic interferometer. Our research shows that five key diagnostic characteristics in phytolith morphology can be used to distinguish Foxtail millet from Common millet based on the presence of cross-shaped type, regularly arranged papillae, Omega-undulated type, endings structures of epidermal long cell, and surface ridgy line sculpture in the former species. We have established identification criteria that, when used together, give the only reliable way of distinguishing between Foxtail millet and Common millet species based on their phytoliths characteristics, thus making a methodological contribution to phytolith research. Our findings also have important implications in the fields of plant taxonomy, agricultural archaeology, and the culture history of ancient civilizations.

Crop classification is one of the most important agricultural applications of remote sensing. Many studies have investigated crop classification using SAR data, while few studies have focused on the classification of dryland crops by the new Gaofen-3 (GF3) SAR data. In this paper, taking Hengshui city as the study area, the performance of the Freeman–Durden, Sato4, Singh4 and multi-component decomposition methods for dryland crop type classification applications are evaluated, and the potential of full-polarimetric GF3 data in dryland crop type classification are also investigated. The results show that the multi-component decomposition method produces the most accurate overall classifications (88.37%). Compared with the typical polarization decomposition techniques, the accuracy of the classification results using the new decomposition method is improved. In addition, the Freeman method generally yields the third-most accurate results, and the Sato4 (87.40%) and Singh4 (87.34%) methods yield secondary results. The overall classification accuracy of the GF3 data is very positive. These results demonstrate the great promising potential of GF3 SAR data for dryland crop monitoring applications.

Soil edaphic characteristics are major drivers of fungal communities, but there is a lack of information on how communities vary with soil depth and landscape position in no-till cropping systems. Eastern Washington is dominated by dryland wheat grown on a highly variable landscape with steep, rolling hills. High-throughput sequencing of fungal ITS1 amplicons was used to characterize fungal communities across soil depth profiles (0 to 100 cm from the soil surface) among distinct landscape positions and aspects across a no-till wheat field. Fungal communities were highly stratified with soil depth, where deeper depths harbored distinct fungal taxa and more variable, less diverse fungal communities. Fungal communities from deep soils harbored a greater portion of taxa inferred to have pathotrophic or symbiotrophic in addition to saprotrophic lifestyles. Co-occurrence networks of fungal taxa became smaller and denser as soil depth increased. In contrast, differences between fungal communities from north-facing and south-facing slopes were relatively minor, suggesting that plant host, tillage, and fertilizer may be stronger drivers of fungal communities than landscape position.

Dryland agriculture nourishes one third of global population, although crop irrigation is often mandatory. As freshwater sources are scarce, treated and untreated wastewater is increasingly used for irrigation. Here, we investigated how the transformation of semiarid shrubland into rainfed farming or irrigated agriculture with freshwater, dam-stored or untreated wastewater affects the total (DNA-based) and active (RNA-based) soil bacterial community composition, diversity, and functionality. To do this we collected soil samples during the dry and rainy seasons and isolated DNA and RNA. Soil moisture, sodium content and pH were the strongest drivers of the bacterial community composition. We found lineage-specific adaptations to drought and sodium content in specific land use systems. Predicted functionality profiles revealed gene abundances involved in nitrogen, carbon and phosphorous cycles differed among land use systems and season. Freshwater irrigated bacterial community is taxonomically and functionally susceptible to seasonal environmental changes, while wastewater irrigated ones are taxonomically susceptible but functionally resistant to them. Additionally, we identified potentially harmful human and phytopathogens. The analyses of 16 S rRNA genes, its transcripts and deduced functional profiles provided extensive understanding of the short-term and long-term responses of bacterial communities associated to land use, seasonality, and water quality used for irrigation in drylands.

Cowpea ( Vigna unguiculata L. Walp., 2n = 2x = 22) is a vital dryland legume crop, renowned for its affordable dietary protein and essential nutrients for humans and animals. Cowpea originated in Africa and spread to various parts of the world through human migration, eventually reaching Asia. However, genetic diversity and structure in Asia cowpea remain poorly understood. This study utilized 6334 SilicoDArT and 14,482 single nucleotide polymorphism (SNP) markers to assess the genetic diversity and population structure of 405 cowpea accessions from 17 different countries, sourced from the National Agriculture and Food Research Organization (NARO) genebank in Japan. We used population structure, principal component analysis, discriminant analysis of principal components, and phylogenetic tree analysis to group the accessions into two main genetic populations. The accessions were further classified into six subgroups of African and Asian populations, corresponding to the geographical origins of the accessions. South Asian accessions showed the highest differentiation, with Nepalese accessions forming a distinct group along with Japanese accessions, highlighting that the rich genetic resources preserved within these regions may harbor valuable traits for breeding. In contrast, Southeast Asian and West African accessions exhibited low to moderate differentiation, suggesting recently shared genetic ancestry. AMOVA demonstrated that most genetic variation existed within accessions, while variation between populations was minimal. These findings highlight the rich genetic potential within the Asian cowpea germplasm, particularly in Nepalese and Japanese accessions. This study provides critical insights into breeding strategies aimed at enhancing the adaptability and productivity of cowpea in diverse environments.

Drought is the most important natural disaster affecting crop growth and development. Crop rhizosphere microorganisms can affect crop growth and development, enhance the effective utilization of nutrients, and resist adversity and hazards. In this paper, six spring wheat varieties were used as research material in the dry farming area of the western foot of the Greater Khingan Mountains, and two kinds of water control treatments were carried out: dry shed rain prevention (DT) and regulated water replenishment (CK). Phenotypic traits, including physiological and biochemical indices, drought resistance gene expression, soil enzyme activity, soil nutrient content, and the responses of potential functional bacteria and fungi under drought stress, were systematically analyzed. The results showed that compared with the control (CK), the leaf wilting, drooping, and yellowing of six spring wheat varieties were enhanced under drought (DT) treatment. The plant height, fresh weight (FW), dry weight (DW), net photosynthetic rate (Pn) and stomatal conductance (Gs), soil total nitrogen (TN), microbial biomass carbon (MBC), microbial biomass nitrogen (MBN), microbial biomass phosphorus (MBP), organic carbon (SOC), and soil alkaline phosphatase (S-ALP) contents were significantly decreased, among which, FW, Gs and MBC decreased by more than 7.84%, 17.43% and 11.31%, respectively. By contrast, the soil total phosphorus (TP), total potassium (TK), and soil catalase (S-CAT) contents were significantly increased (p < 0.05). TaWdreb2 and TaBADHb genes were highly expressed in T.D40, T.L36, and T.L33 and were expressed at low levels in T.N2, T.B12, and T.F5. Among them, the relative expression of the TaWdreb2 gene in T.L36 was significantly increased by 2.683 times compared with CK. Soil TN and TP are the most sensitive to drought stress and can be used as the characteristic values of drought stress. Based on this, a drought-tolerant variety (T.L36) and a drought-sensitive variety (T.B12) were selected to further analyze the changes in rhizosphere microorganisms. Drought treatment and cultivar differences significantly affected the composition of the rhizosphere microbial community. Drought caused a decrease in the complexity of the rhizosphere microbial network, and the structure of bacteria was more complex than that of fungi. The Shannon index and network modular number of bacteria in these varieties (T.L36) increased, with rich small-world network properties. Actinobacteria, Chloroflexi, Firmicutes, Basidiomycota, and Ascomycota were the dominant bacteria under drought treatment. The beneficial bacteria Bacillus, Penicillium, and Blastococcus were enriched in the rhizosphere of T.L36. Brevibacillus and Glycomyce were enriched in the rhizosphere of T.B12. In general, drought can inhibit the growth and development of spring wheat, and spring wheat can resist drought hazards by regulating the expression of drought-related genes, regulating physiological metabolites, and enriching beneficial microorganisms.

BackgroundCereal/legume intercropping can enhance crop productivity and improve soil health in dryland farming. However, little is known about soil biological health under maize/peanut intercropping. The aim of this study was to assess soil biological health based on nematode communities in a maize/peanut intercropping system.MethodsWe conducted a field experiment with different planting patterns, including monoculture maize (M), monoculture peanut (P), and maize intercropped with peanut (IM, intercropped maize; IP, intercropped peanut) to determine the influence on soil biological health. We measured soil physicochemical properties and nematode communities, and employed exploratory factor analysis combined with cumulative normal distribution curve scoring to identify potential soil biological health traits.ResultsThe intercropped maize gave the highest plant parasitic nematode abundance, trophic diversity index, evenness index, and structure index. The monoculture peanut gave the highest enrichment index and least plant parasitic nematode abundance, trophic diversity index, Shannon diversity index, evenness index, structure index, and channel index. We identified four soil biological health traits, including basic nutrients and biodiversity, food web complexity, slow energy channel, and fast energy channel, mainly represented by soil ammonium nitrogen and Shannon diversity index, structural index and omnivore-predator nematode abundance, fungivorous nematode abundance and plant parasitic nematode abundance, microbial biomass carbon and bacterivorous nematode abundance, respectively. The intercropping systems improved the comprehensive score of soil biological health, especially maize intercropping soil. Intercropping maize and intercropping peanut significantly improved soil biological health traits representing the food web complexity compared with the corresponding monoculture soils.ConclusionsOur results indicate that soil nematode and physicochemical indicators reflect different soil biological health traits. Among those traits, the improvement of basic nutrients and biodiversity and the complexity of the food web were the main reasons for improving soil biological health through the intercropping system.

The United Arab Emirates (UAE), a dryland country, has since its independence, emphasized on giant greening projects. Monitoring the trend of greening progress in the UAE has gained importance for environmental management and carbon footprint monitoring. Hence, this study created and analysed a time-series (TS) vegetation map to track and analyse vegetation dynamics over an extended period of fifty years. Study area included three selected desert cities of the UAE, Abu Dhabi (AD) capital city, Dubai city and Al Ain city. Random Forest algorithm was applied on Landsat multi-temporal images from 1972 until 2021 for classifying and monitoring the vegetation dynamics and change trajectories. Four vegetation subclasses (coastal/wetland vegetation, urban vegetation, farms/crop fields, and natural/artificial forests), were assessed then grouped and mapped as one vegetation class. With the adopted approach, we achieved overall classification accuracy ranging from 86% to 94%, with kappa coefficients ranging from 0.7200 to 0.8800. Current study showed that the vegetation cover extent in the UAE was at a constant growth for the past five decades from only 1,231.1 ha in 1972 to 23,176.46 ha in 2021, 19 times folds. Furthermore, it showed that desert cities tend to increase their vegetation cover while continuing their steady urban growth. The other drivers found include demographic increase and governmental policies (granting farms to locals and environmental protection laws). Finally, the approach implemented in this research can effectively and reliably be used in other urban centres for future monitoring and management of the vegetation cover status in the country.