Accurate quantification of soil organic carbon (SOC) is essential for assessing agricultural sustainability and greenhouse gas (GHG) mitigation. However, determining the number of samples needed to capture SOC variability in tropical sugarcane systems is challenging due to soil texture and stratification from controlled traffic farming (CTF). This study estimated the minimum sample size required to assess bulk density (BD), carbon (C) concentration, and SOC stocks (0–100 cm) across three texture classes (<20%, 20–55%, >55% clay + silt), assuming a maximum error of 20% and α = 0.10. Soils were collected from 67 commercial sugarcane fields in south-central Brazil, totaling 2,412 samples at six depths (0–10, 10–20, 20–30, 30–50, 50–70, and 70–100 cm). Variance estimators and bootstrap resampling (10,000 iterations) were used to evaluate spatial variability and sampling requirements. Significant differences between rows and inter-rows were observed in the top 0–30 cm: inter-rows showed 10–20% higher BD, 15–25% lower C concentration, and 10–30% lower SOC stocks. Soil texture strongly influenced variability and sample size. Fine-textured soils (>55% clay + silt) required fewer samples (BD: 3; C: 14; SOC: 14), whereas sandy soils (<20%) required up to 4, 27, and 26 samples, respectively. Variability and sampling needs declined with depth, reflecting localized effects of compaction and residue management. These results provide texture-based guidelines to optimize SOC sampling in sugarcane systems, reducing costs by 30–50% and supporting integration with MRV and certification frameworks, thereby strengthening sustainable production and climate commitments.

Soil application of herbicides for preemergence (PRE) weed control in grain sorghum is vital to control weeds. Efficacy of soil-applied herbicides is impacted by herbicide adsorption which is influenced by soil organic matter (SOM) and texture. With precision agriculture technologies, variable rate applications (VRA) can be utilized to maximize herbicide effectiveness. In 2016, algorithms were developed for two locations to use VRA of two tank mixed herbicides based on SOM and soil electrical conductivity (EC) collected by a Veris MSP3 system. Drone imagery provided an effective way to evaluate the efficacy of herbicide applications along with visual assessment. VRA applications of herbicide tank mixes provided equal weed control compared to flat rate applications.

High spatial variability of soil properties restricts the benefits of process-oriented modelling for management recommendations on field scale due to rare information about the soil inventory and its distribution. Geo-electrical mapping provides with a certain spatial pattern, but results are influenced by various factors. The model HERMES was applied to 60 of altogether 80 soil sampling points of a well-documented field in North-Rhine Westphalia characterised by a wide range of soil texture. Validation of HERMES resulted in satisfactory root mean square errors (RMSE) for yield (0.76 t ha-1), water (34.70 mm) and nitrogen in soil (51.44 kg ha-1) over the whole simulation period. At the same field, values of conducted electrical conductivity (ECa) mapping ranged from 20 to 90 mS m-1 and have been assigned to the soil sampling points to proof statistical relations. Clay and sand contents of three soil layers were highly correlated with measured ECa values. Derived regression models show R2 values between 0.73 and 0.89. The subsequent calculation of soil texture at points of ECa mapping produced an improved resolution of this key value to initialize model simulation.

Remote sensing images offer a unique monitoring capacity for agriculture fields. Despite significant drawbacks, free historical remote sensing images are likely to provide valuable information to delineate temporally stable zones, especially for a perennial crop like vines. In order to test the potential of these free images for delineating within field soil zones, an experiment was performed on three vine fields located in southern France. On each vine field, the grower was asked to delineate within field zones that present differences in soil characteristics. The first expert zoning (ZP zoning) was only based on the grower’s knowledge. Two weeks after, 12 visible free remote sensing were provided to the grower to refine the first zoning (ZAI zoning). In order to assess the relevancy of both zonings, a soil apparent electrical conductivity (ECa) survey was performed on each field. The Rv zoning index was then computed on ECa data to assess the potential improvement between ZP and ZAI zonings. Results show that for two fields, remote sensing images improved the zoning while for one field both ZP and ZAI expert zonings were similar. This result highlights a potential interest in historical remote sensing images to improve the knowledge that growers have of their fields. It should be noted, however, that this improvement is still limited in the case of viticulture where the small size of the fields with a large number of manual operations is likely to lead to a good knowledge of within field variability.

The effects of considering variable within-farm soil runoff and leaching potential on costs of reducing nitrogen losses are analyzed for a Virginia dairy. Manure applications may cause nitrogen losses through runoff and leaching because of factors such as uncertain nitrogen mineralization. Farmers can reduce nitrogen control costs by applying manure on soils with less nitrogen loss potential. Ignoring within-farm soil variability may result in overstating the farm's costs of reducing nitrogen losses.