The hydrological characteristics of gravel-containing soils are different from those of gravel-free soils, so it is worth further understanding and enriching the theory of soil and water conservation. In this study, adjustable slope (10°, 20°, 30°) test soil boxes with different surface gravel contents (0%, 25%, 50%, 75%, 100%) were prepared to study the runoff erosion characteristics of gravel-covered land slopes under different rainfall conditions (10 mm/h, 20 mm/h, 30 mm/h). Compared with the bare soil, the runoff start time of the three slopes covered with 100% soil surface gravel content is delayed by 38.90, 32.83 and 73.39%, the runoff producing rate of gravel condition under different slopes decreased by 7.20–71.52% and the total amount of sediment yield decreased by 7.94~84.57%. Surface gravel cover can effectively reduce runoff and sediment yield, which is beneficial for better soil and water conservation. The results of this study have a certain reference value for the theory of soil and water conservation and can be used as a basis for guiding efficient agricultural production in gravel-mulched land and construction (like road slope improvement).

This study sought to evaluate the potential of mulched gravel to release nutrients in the field by conducting trials with three variations of wet-and-dry cycling of the soil beneath gravel mulch and bare soil. The results revealed that quartz, muscovite, clinochlore, and albite were the most abundant minerals in the gravels. Throughout the whole wet-and-dry cycle, the total content of 30 elements measured in the gravel-mulched soil was higher than in the bare soil treatment, and the content of the total element rose with increasing wet–dry cycle humidity. The enrichment ratio (B_r) of each element in soil under gravel mulch relative to bare soil was in the sequence Mg > Ca > K > Cr > Na > Mn > V > Zn > Fe > Ti > Si > C > N > B> Co > (B_r = 0) > Pb > Cu > Ga > P > Sn > Sr > Al > Be > Li > Mo > Ni > Se > As > S. Under gravel mulch, the elements impacted by the wet–dry cycle are primarily rock-forming, whereas the elements affected under bare soil are primarily trace elements. The wet-and-dry cycles of gravel affected soil nutrients mainly by increasing soil K, Ca, Na, and Mg contents. The differences in soil K and Ca contents significantly affected the growth of jujube trees and the jujube fruit yield. A dry/wet cycle level of 5 L/d per tree under gravel cover conditions can effectively improve soil nutrients and increase the jujube fruit yield without causing environmental problems.

In the arid and semi-arid regions of Northwest China, periodic rainfall deficits, high field evaporation, limited freshwater resources, and high irrigation costs restrict crop yield and water productivity (WP). Gravel mulching (GM), a traditional agricultural tillage management practice widely used in arid and semi-arid regions, improves crop yield and WP. However, the combined impacts of GM on crop yield and WP are unclear. This study aimed to examine the effects of GM on crop yield and WP under different factors and to find the most critical regional factors and gravel characteristics that affect crop yield and WP. To quantitatively assess the impact of GM on crop yield and WP, this study performed a meta-analysis, a regression analysis, and a path analysis of 185 yield comparisons and 130 WP comparisons from 30 peer-reviewed scientific reports. This study found that GM significantly increased crop yield and WP by an average of 29.47% and 28.03%, respectively. GM was reported with the highest response percentages (I) of crop yield and WP in regions whose average annual precipitation (AAP) was 200–400 mm, average annual temperature (AAT) was 0–9 °C, and altitude (A) was >1000 m. Overall, AAP, AAT, and A had significant effects on the I of crop yield (p < 0.001), but AAT and A had an insignificant impact on the I of crop WP (p > 0.05). Gravel size (GS), the amount of gravel mulching (AGM), the degree of gravel mulching (DGM), and the gravel mulching thickness (GMT) had a significantly positive impact on crop yield and WP (p < 0.05). The stepwise multiple linear regression analysis results indicated that the primary regional factors influencing yield were AAT and A, contributing 43.14% and 53.09%, respectively. GMT and GS were identified as significant gravel characterization factors impacting yield, contributing 82.63% and 17.37%, respectively. AAP and GMT were the main regional factors and gravel characterization factors affecting WP. Furthermore, the I values for cash crop yield and WP were higher than that for food crops, and moderate fertilization and irrigation would increase the I values of yield and WP. The benefits of GM are strongly correlated with the planting year. This study’s results show that GM generally improves crop yield and WP, although the extent of this impact varies based on different conditions. These findings are not only useful in relation to their direct applicability to other countries worldwide but also due to their potential to provide new ideas for agricultural practices in similar crop-growing environments.

Getting accurate and up-to-date information on the cultivated land area and spatial arrangement of maize, an important staple crop in the Ningxia Hui Autonomous Region, is very important for planning agricultural development in the region and judging crop yields. This work proposes a machine-learning methodology to extract corn from medium-resolution photos obtained from the Sentinel-2 satellite. The Google Earth Engine (GEE) cloud platform is utilized to facilitate the process. The identification of maize cultivation regions in Huinong District in the year 2021 was performed through the utilization of support vector machine (SVM) and random forest (RF) classification techniques. After obtaining the results, they were compared to see if using the random forest classification method to find planting areas for maize was possible and useful. Subsequently, the regions where maize was cultivated were combined with image data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which has a high temporal resolution. The Normalized Difference Vegetation Index (NDVI) contemporaneous difference method, which gives regular updates, was then used to track the growth of maize during its whole growth phase. The study’s results show that using the GEE cloud platform made it easier to quickly map out data about where to plant maize in Huinong District. Furthermore, the implementation of the random forest method resulted in enhanced accuracy in extracting maize planting areas. The confusion matrix’s evaluation of the classification performance produced an average overall accuracy of 98.9% and an average Kappa coefficient of 0.966. In comparison to the statistics yearbook of the Ningxia Hui Autonomous Region, the method employed in this study consistently yielded maize-planted area estimates in Huinong District with relative errors below 4% throughout the period spanning 2017 to 2021. The average relative error was found to be 2.04%. By combining MODIS image data with the NDVI difference model in the year 2021, the high-frequency monitoring of maize growth in Huinong District was successful. The growth of maize in Huinong District in 2021 exhibited comparable or improved performance in the seedling stage, nodulation stage, and the early stage of staminate pulling and spitting, possibly attributed to the impact of climate and other relevant elements. After that, the growth slowed down in August, and the percentage of regions with slower growth rates than in previous years gradually increased. However, overall, the growth of maize in Huinong District during the year 2021 showed improvement relative to the preceding years. The present study introduces a novel approach that demonstrates the capability to accurately extract corn crops in the Huinong District while simultaneously monitoring their growth at a high frequency.