Search Results (3)

Understanding farmers’ perceptions of soil health is valuable for developing strategies to increase the adoption of conservation practices. A combination of soils with poor soil fertility, low levels of soil organic matter, and the use of production practices that, although necessary for vegetable production, could negatively impact soil health makes the exploration of how large-scale vegetable producers in Tennessee perceive soil health and manage soils interesting. Using information from semi-structured interviews with operators of three Tennessee large-scale vegetable farms, we explored farmers’ perceptions of soil health and how those perceptions connect with adoption decisions. Our results suggest that farmers’ perceptions of soil health reflect a broad perspective that includes crop productivity and disease pressure. Profitability exerted a stronger influence on farmers’ decision-making than soil health. Nonetheless, farmers recognized that there is an association between soil health and profitability. The farmers included in this study found value in the information provided by soil health tests to confirm the benefits of soil management practices. The results presented in this study will contribute to the design of future studies aiming to investigate the relationship between farmer perceptions of soil health and the adoption of best soil management practices among large-scale vegetable growers. Full article

The health status of Quaternary red soil is a comprehensive reflection of the production and ecological service functions, which directly affects agricultural productivity and ecosystem sustainability. Based on the Cornell Soil Health Assessment (CASH) system frame, a health evaluation system for Quaternary red soils was established including the soil’s physical, chemical, and biological indicators. The soil’s health status under different land use patterns (the buried Quaternary red soil, sparse forest and grassland, grassland, woodland, and arable land) was systematically diagnosed in the low hilly region of western Liaoning Province. The results showed significant differences in the soil health comprehensive index of the Quaternary red soils under different land use patterns (the whole soil), presenting a trend of woodland (0.64) > arable land (0.61) > grassland (0.49) > sparse forest and grassland (0.37) > buried Quaternary red soils (0.33). The woodland and arable land are at a healthy level, the grassland and sparse forest and grassland are at a sub-healthy level, and the buried Quaternary red soil is at an unhealthy level. The health status of the topsoil layer (A) under different land use patterns has a trend of woodland (0.86) > arable land (0.73) > grassland (0.70) > sparse forest and grassland (0.67). This is consistent with the overall health status of the profile, better than that of subsoil layer (B), which presents a trend of arable land (0.41) > grassland (0.40) > woodland (0.38) > sparse forest and grassland (0.34), with relatively poor soil health conditions. Overall, the soil health status of the four land use patterns is better than that of the buried Quaternary red soils, showing an evolution trend towards healthy soil. This indicates that at this stage, human land use activities have to some extent promoted the healthy development of Quaternary red soils. The Quaternary red soils of the woodland have a healthy status, and the land use pattern is suitable and can be scientifically recommended in low mountain and hilly areas. Full article

Sensor technology can be a reliable and inexpensive means of gathering soils data for soil health assessment at the farm scale. This study demonstrates the use of color system readings from the Nix Pro^TM color sensor (Nix Sensor Ltd., Hamilton, ON, Canada) to predict soil organic carbon (SOC) as well as total nitrogen (TN) in variable, glacial till soils at the 147 ha Cornell University Willsboro Research Farm, located in Upstate New York, USA. Regression analysis was conducted using the natural log of SOC (lnSOC) and the natural log of TN (lnTN) as dependent variables, and sample depth and color data were used as predictors for 155 air dried soil samples. Analysis was conducted for combined samples, Alfisols, and Entisols as separate sample sets and separate models were developed using depth and color variables, and color variables only. Depth and L* were significant predictors of lnSOC and lnTN for all sample sets. The color variable b* was not a significant predictor of lnSOC for any soil sample set, but it was for lnTN for all sample sets. The lnSOC prediction model for Alfisols, which included depth, had the highest R² value (0.81, p-value < 0.001). The lnSOC model for Entisols, which contained only color variables, had the lowest R² (0.62, p-value < 0.001). The results suggest that the Nix Pro^TM color sensor is an effective tool for the rapid assessment of SOC and TN content for these soils. With the accuracy and low cost of this sensor technology, it will be possible to greatly increase the spatial and temporal density of SOC and TN estimates, which is critical for soil management. Full article