Soil health is essential for sustainable agricultural operations, as it supports farm production and ecosystem services. The adoption of sustainable agriculture practices such as conservation tillage, cover cropping, and crop rotation provides significant benefits for both crop productivity and environmental sustainability. These practices can increase soil biodiversity, nutrient cycling, and organic matter, which increase the resilience of agroecosystems. This narrative review synthesizes the insights of the soil health practices adoption literature, with a focus on common farming practices that can improve soil health and enhance crop yields, reviewing the results of various approaches and pointing out the challenges and opportunities for implementing sustainable agriculture on a larger scale. This paper discusses the effects of various tillage and cropping system approaches on soil health, including no-till and conventional tillage systems, crop rotation, cover cropping, cultivator combinations, and fertilizer application. This study found that conservation tillage is more beneficial to soil health than conventional tillage—which is still debated among scientists and farmers—and that different tillage methods interact differently. In contrast, agricultural yields increase more with intercropping, crop rotation, and cover crops than monocropping. For maintaining soil fertility, this study shows that agricultural yields could be increased by implementing zero tillage. This review identifies the most suitable farming practices for improving soil health while boosting crop production with minimal negative impact on the soil. It also highlights the benefits of these practices in maintaining soil quality.

Waterlogging in agriculture poses severe threats to soil properties, crop yields, and farm profitability. Remote sensing data coupled with drainage systems offer solutions to monitor and manage waterlogging in agricultural systems. However, implementing agricultural projects such as drainage is associated with high uncertainty and risk, with substantial negative impacts on farm profitability if not well planned. Cost–benefit analyses can help allocate resources more effectively; however, data scarcity, high uncertainty, and risks in the agricultural sector make it difficult to use traditional approaches. Here, we combined a wide range of field and remote sensing data, unsupervised machine learning, and Bayesian probabilistic models to: (1) identify potential sites susceptible to waterlogging at the farm scale, and (2) test whether the installation of drainage systems would yield a positive benefit for the farmer. Using the K-means clustering algorithm on water and vegetation indices derived from Sentinel-2 multispectral imagery, we were able to detect potential waterlogging sites in the investigated field (elbow point = 2, silhouette coefficient = 0.46). Using a combination of the Bayesian statistical model and the A/B test, we show that the installation of a drainage system can increase farm profitability by 1.7 times per year compared to the existing farm management. The posterior effect size associated with yield, cropping area, and time (year) was 0.5, 1.5, and 1.9, respectively. Altogether, our results emphasize the importance of data-driven decision-making for agriculture project planning and resource management in the wake of smart agriculture for food security and adaptation to climate change.