Reflectance spectroscopy for soil property prediction is a non-invasive, fast, and cost-effective alternative to the standard laboratory analytical procedures. Soil spectroscopy has been under study for decades now with limited application outside research. The recent advancement in precision agriculture and the need for the spatial assessment of soil properties have raised interest in this technique. The performance of soil spectroscopy differs from one site to another depending on the soil’s physical composition and chemical properties but it also depends on the instrumentation, mode of use (in-situ/laboratory), spectral range, and data analysis methods used to correlate reflectance data to soil properties. This paper uses the systematic review procedure developed by the Centre for Evidence-Based Conservation (CEBC) for an evidence-based search of soil property prediction using Visible (V) and Near-InfraRed (NIR) reflectance spectroscopy. Constrained by inclusion criteria and defined methods for literature search and data extraction, a meta-analysis is conducted on 115 articles collated from 30 countries. In addition to the soil properties, findings are also categorized and reported by different aspects like date of publication, journals, countries, employed regression methods, laboratory or in-field conditions, spectra preprocessing methods, samples drying methods, spectroscopy devices, wavelengths, number of sites and samples, and data division into calibration and validation sets. The arithmetic means of the coefficient of determination (R²) over all the reports for different properties ranged from 0.68 to 0.87, with better predictions for carbon and nitrogen content and lower performance for silt and clay. After over 30 years of research on using V-NIR spectroscopy to predict soil properties, this systematic review reveals solid evidence from a literature search that this technology can be relied on as a low-cost and fast alternative for standard methods of soil properties prediction with acceptable accuracy.

The shortage and uneven spatial and distribution of agricultural water resources has greatly restricted the sustainable development of regional society and economy. In this study, an improved five-element connection number set pair analysis model, which subdivides the same–different–opposite connection number in the set pair analysis theory to enhance the integrity and effectiveness of the original ternary connection numbers is constructed to evaluate the agricultural water resources carrying capacity (AWRCC) in the Haihe River Basin. Based on this evaluation result, an optimization model (AROL model) is proposed to optimize the effective irrigation area and groundwater exploitation to achieve a “better level” of AWRCC. The evaluation result shows that the current AWRCC of the Haihe River Basin is relatively low. The AWRCC in Shanxi, Inner Mongolia, and Liaoning is Level III and the current agricultural water resources are not overloaded but have little carrying potential. The AWRCC of Beijing, Tianjin, Hebei, Henan, and Shandong are rated IV and overloaded, among which Shandong has the lowest comprehensive score and the most serious overload. The optimization result shows that the extraction and conservation of groundwater in most areas of the Haihe River Basin is unbalanced and the effective irrigation area needs to be increased. With different current conditions in different areas, the groundwater exploitation and the effective irrigation area is adjusted correspondingly. Among the areas, the adjustment of groundwater exploitation and the effective irrigation area in Hebei are the most significant.

Optimizing regional crop water consumption is considered to be a significant approach for increasing yields and reducing water consumption. This paper proposes a single-objective linear programming model which couples the distributed water consumption model with crop suitability. The impacts of meteorological, topographic, and soil factors were taken into account in both the distributed water consumption model and the crop suitability. The developed model was applied to a real case study in the middle reaches of Heihe River basin, in the northwest of China. In the optimization model, the net benefit which combined the water consumption with crop suitability was regarded as the objective function, while the limits on available water and planting area were set as the constraints. Optimal results regarding crop distribution and water consumption were generated for dry, normal, and wet hydrological years. Two optimization strategies were analyzed, including one with a fixed area of each crop and the other with a fixed total planting area. Economic analyses showed that net income under both optimization strategies increased by 31% and 33%, respectively. Although water consumption increased slightly in both optimization scenarios, the unit water income and unit area income were much higher than in the pre-optimization conditions. The obtained results are valuable for supporting the adjustment of planting patterns and the identification of desired plans for sustainable irrigation water allocation.

The recent surge in dam construction has sparked debates regarding their contribution to carbon neutrality and food security, focusing on trade-offs between production benefits and ecological drawbacks. However, how dams affect carbon emissions and land cover changes, including their spatial differentiations, remains unclear. We quantified spatiotemporal variations in carbon emissions and storage of 137 large dams in China from 1992 to 2020, resulting from land cover change in potentially affected areas. We observed a lesser increase in carbon emissions and a more pronounced increase in carbon storage driven by forest conservation and regeneration within dam-affected areas compared to unaffected areas. Additionally, we noticed an increased grain yield in nearby areas potentially due to increased water availability. Our findings highlight the importance of considering land cover change when assessing carbon neutrality or grain yield at regional and national scales. This study provides useful insights into optimizing dam locations to mitigate future carbon emissions effectively.