Here we study the precipitation of lead (Pb)-phosphate minerals over the pH range of 4.0 to 8.0 with and without oxalate, a ubiquitous and abundant low-molecular-weight organic acid derived from plants and microorganisms.

A Partial Least Squares (PLS) carbonate (CO 3 ) prediction model was developed for soils throughout the contiguous United States using mid-infrared (MIR) spectroscopy. Excellent performance was achieved over an extensive geographic and chemical diversity of soils. A single model for all soil types performed very well with a root mean square error of prediction (RMSEP) of 12.6 g kg -1 and was further improved if Histosols were excluded (RMSEP 11.1 g kg -1 ). Exclusion of Histosols was particularly beneficial for accurate prediction of CO 3 values when the national model was applied to an independent regional dataset. Little advantage was found in further narrowing the taxonomic breadth of the calibration dataset, but higher precision was obtained by running models for a restricted range of CO 3 . A model calibrated using only on the independent regional dataset, was unable to accurately predict CO 3 content for the more chemically diverse national dataset. Ten absorbance peaks enabling CO 3 prediction by mid-infrared (MIR) spectroscopy were identified and evaluated for individual and combined predictive power. A single-band model derived from an absorbance peak centered at 1796 cm - yielded the lowest RMSEP of 13.5 g kg -1 for carbonate prediction compared to other single-band models. This predictive power is attributed to the strength and sharpness of the peak, and an apparent minimal overlap with confounding co-occurring spectral features of other soil components. Drawing from the 10 identified bands, multiple combinations of 3 or 4 peaks were able to predict CO 3 content as well as the full-spectrum national models. Soil CO 3 is an excellent example of a soil parameter that can be predicted with great effectiveness and generality, and MIR models could replace direct laboratory measurement as a lower cost, high quality alternative.

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Core Ideas SOC stocks are highly variable at the agroecosystem scale. Optimized sampling and estimation approaches are crucial for low cost SOC assessment. Systematic sampling provided thorough geographic and attribute space coverage. Ordinary kriging outperformed regression kriging, simple mean, and SSURGO approaches. Low‐cost approaches for measuring soil organic C (SOC) stocks are essential for verifying farm management effects on C sequestration in agroecosystems. This study compared sampling and data analysis optimization approaches for estimating SOC stocks of a complex agroecosystem. Soil samples were collected from a 232‐ha area of a working dairy farm with multiple land uses, crop rotations, topographic features, and manure application rates. The SOC stocks were estimated by (i) simple mean, (ii) ordinary kriging, (iii) regression kriging, and (iv) using the USDA Soil Survey Geographic (SSURGO) database. Relationships among sampling schemes, estimation approaches, auxiliary information, and SOC stocks were explored. Slope, elevation, soil type, and land use types displayed a high degree of variation at the study site, yet relationships with SOC stocks were weak or nonsignificant. Systematic sampling provided the best coverage of both geographic and attribute space, allowing reliable variogram estimates and RMSEs that increased little with reduced sample number. Random and stratified random sampling approaches resulted in reduced accuracy. Ordinary kriging had lower RMSEs than either regression kriging or the simple mean, and total SOC stocks estimates fluctuated less when sample sizes were reduced. Using SSURGO overestimated total SOC stocks by up to 5.1% compared with the other approaches, suggesting that this approach may be worthy of evaluation for circumstances with a low budget and low confidence level requirements. Systematic sampling and ordinary kriging can provide an optimal strategy for estimating SOC stocks in agroecosystems with complex topography and land uses.

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Sub-optimal wheat productivity in the eastern Indo-Gangetic plain of India can largely be attributed to delayed sowing and the use of short duration varieties. The second week of November is the ideal time for sowing wheat in eastern India, though farmers generally plant later. Late-sowing farmers tend to prefer short-duration varieties, leading to additional yield penalty. To validate the effect of timely sowing and the comparative performance of long- and short-duration varieties, multi-location on-farm trials were conducted continuously over five years starting from 2016–2017. Ten districts were selected to ensure that all the agro-climatic zones of the region were covered. There were five treatments of sowing windows: (T1) 1 to 10 November, (T2) 11–20 November, (T3) 21 to 30 November, (T4) 1–15 December, and (T5) 16–31 December. Varietal performance was compared in T3, T4, and T5, as short-duration varieties are normally sown after 20 November. There is asymmetry in the distribution of samples within treatments and over the years due to the allocation of fields by farmers. Altogether, the trial was conducted at 3735 sites and captured 61 variables, including yield and yield attributing traits. Findings suggested that grain yields of long-duration wheat varieties are better even under late sown scenarios.

In large‐scale arable cropping systems, understanding within‐field yield variations and yield‐limiting factors are crucial for optimizing resource investments and financial returns, while avoiding adverse environmental effects. Sensing technologies can collect various crop and soil information, but there is a need to assess whether they reveal within‐field yield constraints. Spatial data regarding grain yields, proximal soil sensing data, and topographical and soil properties were collected from 26 maize (Zea mays L.) growing fields in the U.S. Mid‐Atlantic. Apparent soil electrical conductivity (ECa) collected by an on‐the‐go sensor (Veris) was an effective method for estimating subsoil textural variation and water holding capacity in the Coastal Plain region, which was also the best predictor of spatial yield pattern when combined with surface pH and topographic wetness index in a Random Forest (RF) model. In the Piedmont Plateau region, proximal soil sensors showed a lower correlation to measured soil properties, while topographical properties (aspect and slope) were important estimators of spatial yield patterns in an RF model. In locations where the RF model failed to predict yield variation, soil compaction appeared to be limiting crop yields. In conclusion, the application of RF models using ECa sensors and topographical properties was effective in revealing within‐field yield constraints, especially in the Coastal Plain region. On the Piedmont Plateau, the calibration of proximal sensor information needs to be improved with a particular focus on soil compaction.