Conservation Agriculture Research Updates - April 2026

This paper describes a way to map accurately the area and fields in Belgium that practice CA management. They point out that monitoring of CA adoption is usually dependent on farmer declarations or field inspections that are not very accurate. In this paper 247 fields using CA in 2020-2021 were used to develop a classification model for predicting CA by combining remote sensing and census data. The census data provided the data for annual crops and cereals in the rotation. The paper explains what remote sensing was used to measure the extent of soil cover, soil disturbance, to construct a classification model distinguishing fields under conservation from those under conventional practices. Their results showed 15.5 % (2875 fields) out of 18,516 cropland fields can be classified as conservation agriculture. These fields tend to adopt non-inversion tillage and have diverse crop rotations.

This study used multiple satellite-derived indices and environmental drivers to infer the level of tillage intensity and identify the presence of cover crops in eastern South Dakota using  environmental drivers acquired from different remote sensing datasets for 2022 and 2023 to map conservation agriculture practices. They successfully detected the presence of cover crops and the tillage intensity in the study region. Their analysis shows the percent use of cover crops in maize and soybeans and adoption of CA tillage practices. This approach benefits both public and private sector organizations by enabling them to track landscapes remotely and efficiently. This, in turn, can incentivize farmers to adopt conservation practices, contributing to climate change mitigation efforts and fostering sustainable agricultural development.

This paper looks at maize yield impacts conservation tillage (CA?) from satellite data from 4 States of the US Corn Belt from 2008-2020. Data was obtained from several thousand fields with differences in climate, soil quality and irrigation status. Their results show overall that long-term adoption of CA increased rainfed maize yields by almost 10% in the area covered. When analyzing the variables, the increase in maize yields were associated with improved water infiltration and retention. But many fields that could benefit from no-till have not adopted yet. They can now strengthen the reasons and areas suitable for benefits from CA. Benefits can be obtained without negative crop yields in most cases. They did say in the paper that NT combined with cover crops amplified the benefits.

This paper looks at the potential and eligibility of CA practices to get carbon credit generation in India. They used farmer surveys and remote sensing data for this research. They also used various additionality conditions in their assessment. Their results show that CA does have the potential to increase farmers' carbon credit earnings. They discuss Punjab's ban on residue burning affects on carbon credits and suggests a 60% increase in carbon prices are required to encourage more adoption of CA. 

This is a unique paper that looks at trends in remote sensing applied to CA that uses a systematic review (PRISMA) methodology to look at the last 30 years of thematic research, development, and trends associated with remote sensing technologies and methods applied to conservation agriculture research at various spatial and temporal scales. 188 articles were used with 68 selected for final analysis grouped into cover crops, crop residue, rotation, mulching and tillage. CA research using remote sensing have been increasing since 1991 and peaked at 10 publications in 2020. The paper offers a summary of future research needs for remote sensing in CA management.

This in an interesting paper that uses the Sentinel-2 satellite to identify field level tillage practices of smallholder farmers in the Indo-Gangetic Plains of India. One weakness about NT and CA is getting an accurate estimate of adoption. This paper suggest that remote sensing can help. They find that tillage practices can be classified with moderate accuracy (an overall accuracy of 75%), particularly in regions with relatively large field sizes and homogenous crop management practices. They also find that satellite data from only the first half of the growing season perform as well as models that use data throughout the growing season, allowing for the creation of within-season tillage maps even in smallholder systems where field sizes are small and cropping practices are heterogeneous.

This paper tested whether cover crops are a suitable management tool to reduce fertilizer input, tillage intensity and herbicide use in Swiss arable cropping systems. They compared the effects of four different cover crop treatments (fallow, radish, subterranean clover and hairy vetch) on maize at two fertilization levels combined with three levels of tillage intensity. They used spectral NVDI imagery to assess vegetation. Cover crops on average increased yields by 12 % (+7 % to +20 %) and cover crop effects depended on tillage intensity, fertilization level and cover crop treatment for most of the assessed maize parameters. Hairy vetch was the best cover crop. Spectral imagery analysis showed that legume cover crops compensated for delayed N availability in reduced and no tillage systems and cover crops contributed to enhanced N uptake and crop growth later in the season. They  provide evidence that cover crop based cropping systems can be used to reduce synthetic inputs and tillage without compromising yield.

This review looks at several ways to improve soil physical health. They define this as physical, chemical and biological characteristics. It includes structure, porosity,  and water retention. They include  soil management like CA, cover crops, and organic amendments. They also suggested precision farming and remote sensing as ways to monitor and manage soil health. They found they had constraints to undertake this and so recommend that future efforts focus on multidisciplinary research to better understand complex relationships.

This is an interesting paper from France uses drones (UAV's) with remote sensing to produce images that can be used to assess the impact of different and temperature distribution and compare NDVI and MTVI2 dynamics as a function of their illuminance. They can do a good job of separating vegetation, no-vegetation, shade, and sun. The paper presents data from two adjacent maize plots that have used conventional (CT) and conservation (CA) agriculture practices. The non-vegetated areas had increased NVDI values as a result of the crop residues in CA plots.

This is an interesting paper presented at a Symposium in California  that presents results of their remote sensing research to monitor CA adoption in Southern Italy. They used an algorithm based on Sentinel-1 and Sentinel-2 data to identify tillage changes over agricultural fields at approximately 100m resolution. They used this technique to monitor fields where Conservation Agriculture has no-tillage as a main principle. They conclude that the accuracy level (better than 80%) derived from a comparison with ground truth data provides a useful tool for practical applications.

This paper reports on the use of remote sensing technology for mapping and monitoring conservation agriculture in China that provides spatial and temporal coverage at a low cost over the past 10 years. The challenges and future prospects of CT remote sensing monitoring advancement are summarized. They conclude that the results provide a holistic picture to improve the understanding of CT remote sensing mapping and monitoring and provide a scientific reference for the field of conservation agriculture remote sensing.

This remote sensing paper uses Electrical Resistivity Tomography (ERT) and Electro Magnetic Induction (EMI) methods to assess the  differences in soil water distribution caused by short- and long-term use of Conventional (CT) and Conservation (CA) practices. Both ERT and EMI were able to distinguish differences between these two practices while traditional direct measurements were not and lacked spatial resolution. ERT showed that CA was more homogeneous and was more sensitive to changes in water content whereas CT soil was more heterogeneous and water distribution was more irregular and difficult to predict. They conclude that for CT soil, the accessible water for the plant is clearly controlled by the soil heterogeneities rather than by the forcing atmospheric conditions. This study paves the way for more refined hydrology models to identify which soil parameters are key to controlling spatial and temporal changes in soil water content.

This study estimated the the spatial-temporal variations of maize yields from 13-year on-farm trials from 4 countries in Southern Africa. Agronomic data from long-term CA trials is used with gridded biophysical and socioeconomic variables. Comparisons were made between CA and CT practices with above and below average precipitation. The variable importance analysis showed that the altitude, precipitation, temperature, and soil physical and nutrients conditions variables explained most of the variation in maize grain yield. Maps were generated to identify the locations where CA had a yield advantage over CP during seasons with below and above-average precipitation. The paper concludes that multi-source remotely sensed data, coupled with advanced and efficient machine learning algorithms provides accurate, cost-effective, and timely platforms for predicting the optimal locations for upscaling sustainable agricultural technologies.