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Bioenergy carbon capture and storage (BECCS) systems can serve as decarbonization pathways for climate mitigation. Perennial grasses are a promising second‐generation lignocellulosic bioenergy feedstock for BECCS expansion, but optimizing their sustainability, productivity, and climate mitigation potential requires an evaluation of how nitrogen (N) fertilizer strategies interact with greenhouse gas (GHG) and soil organic carbon (SOC) dynamics. Furthermore, crop and fertilizer choice can affect the soil microbiome which is critical to soil organic matter turnover, nutrient cycling, and sustaining crop productivity but these feedbacks are poorly understood due to the paucity of data from certain agroecosystems. Here, we examine the climate mitigation potential and soil microbiome response to establishing two functionally different perennial grasses, switchgrass (Panicum virgatum, C4) and tall wheatgrass (Thinopyrum ponticum, C3), in a cool semi‐arid agroecosystem under two fertilizer applications, a novel cyanobacterial biofertilizer (CBF) and urea. We find that in contrast to the C4 grass, the C3 grass achieved 98% greater productivity and had a higher N use efficiency when fertilized. For both crops, the CBF produced the same biomass enhancement as urea. Non‐CO2 GHG fluxes across all treatments were low and we observed a 3‐year net loss of SOC under the C4 crop and a net gain under the C3 crop at a 0–30 cm soil depth regardless of fertilization. Finally, we detected crop‐specific changes in the soil microbiome, including an increased relative abundance of arbuscular mycorrhizal fungi under the C3, and potentially pathogenic fungi in the C4 grass. Taken together, these findings highlight the potential of CBF‐fertilized C3 crops as a second‐generation bioenergy feedstock in semi‐arid regions as a part of a climate mitigation strategy. [ABSTRACT FROM AUTHOR]

Abstract: We used an object-oriented approach in conjunction with the Random Forest algorithm to classify agricultural practices, including tillage (till or no-till (NT)), crop intensity, and grassland-based conservation reserve (CR). The object-oriented approach allowed for per-field classifications and the incorporation of contextual elements in addition to spectral features. Random Forest is a classification tree-based advanced classifier that avoids data over-fitting associated with many tree-based models and incorporates an unbiased internal classification accuracy assessment. Landsat satellite imagery was chosen for its continuous coverage, cost effectiveness, and image accessibility. Classification results for 2007 included producer''s accuracies of 91% for NT and 31% for tillage when applying Random Forest to image objects generated from a May Landsat image. Low classification accuracies likely were attributed to the misclassification of conservation-based tillage practices as NT. Results showed that the binary separation of tillage from NT management is likely not appropriate due to surface spectral and textural similarities between NT and conservation-type tillage practices. Crop and CR lands resulted in producer''s accuracies of 100% and 90%, respectively. Crop and fallow producer''s accuracies were 95% and 82% in the 2007 classification, despite post-senesced vegetation; misclassification within the fallow class was attributed to pixel-mixing problems in areas of narrow (<100 m) strip management. A between-date normalized difference vegetation index approach was successfully used to detect areas having “changed” in vegetation status between the 2007 and prior image dates; classified “changed” objects were then merged with “unchanged” objects to produce crop status maps. Field crop intensity was then determined from the multi-year analysis of generated crop status maps. [Copyright &y& Elsevier]

Abstract: The Kyoto Protocol entering into force on 16 February 2005 continues to spur interest in development of carbon trading mechanisms internationally and domestically. Critical to the development of a carbon trading effort is verification that carbon has been sequestered, and field level measurement of C change is likely cost prohibitive. Estimating C change based on agricultural management practices related to carbon sequestration seems more realistic, and analysis of satellite imagery could be used to monitor and verify these practices over large areas. We examined using Landsat imagery to verify crop rotations and quantify crop residue biomass in north central Montana. Field data were collected using a survey of farms. Standard classification tree analysis (CTA) and boosted classification and regression tree analysis (BCTA) were used to classify crop types. Linear regression (LM), regression tree analysis (RTA), and stochastic gradient boosting (SGB) were used to estimate crop residue. Six crop types were classified with 97% accuracy (BCTA) with class accuracies of 88–99%. Paired t-tests were used to compare the difference between known and predicted mean crop residue biomass. The difference between known and predicted mean residues using SGB was not different than 0 (p-value=0.99); however root mean square error (RMSE) was large (1981kgha−1), implying that SGB accurately predicted regional crop residue biomass but not local predictions (i.e., field or farm level). The results of this study, and previous research classifying tillage practices and estimating soil disturbance, supports using satellite imagery as an effective tool for monitoring and verifying agricultural management practices related to carbon sequestration over large areas. [Copyright &y& Elsevier]

Abstract: Management of agricultural soils, most notably tillage, influences wind, and water erosion, which in turn has implications for non-point source pollution of pesticides, fertilizer, and sediment in agro-ecosystems. No-till (NT) practices improve soil, water, and aquatic ecosystem quality by reducing soil erosion and chemical runoff. The ability of cropland soils to sequester C from the atmosphere might help mitigate global warming. Classification of Landsat ETM+ satellite images has the potential to identify tillage practices and soil disturbance over large areas, enabling efficient monitoring of these agricultural practices. Previous studies predicting tillage management had relatively small study areas (located in a single county), relatively low numbers of fields (6–51), and were temporally focused on non-planted fields to reduce the potential effects of crop canopy interference and/or field patterning. Our objectives were to predict in the presence of crop canopy and over a spatially large, management diverse study area (1) tillage systems (NT versus tilled) and (2) soil disturbance. A farm survey of the study area, north central Montana, was used to as a means to obtain extensive field-level farm management data. We compared logistic regression (LR), traditional classification tree analysis (CTA), and boosted classification tree analysis (BCTA) for identifying NT fields. Logistic regression had an overall accuracy of 94%, BCTA 89%, and CTA 87%, but tillage was not well distinguished. Soil disturbance was estimated using linear regression (LM), regression tree analysis (RTA), and stochastic gradient boosting (SGB), an RTA variant. Classification of soil disturbance was best achieved using RTA (predicted mean soil disturbance not significantly different than known soil disturbance, p-value=0.08). Classification of Landsat ETM+ imagery showed promise for predicting tillage and agricultural soil disturbance over large, heterogeneous areas. [Copyright &y& Elsevier]

Abstract: There has been growing interest in the use of diffuse reflectance as a quick, inexpensive tool for soil characterization. Some studies, using techniques like Partial Least Squares (PLS) regression of 1st derivative spectra have reported predictive accuracies for soil Organic C (OC) and Inorganic C (IC) that approach the analytical limits of standard laboratory measures. We applied 1st derivative Visible and Near-Infrared (VNIR) reflectance PLS regression modeling to soil samples obtained from six sites with similar soils across three counties in north central Montana, with five completely random 30% test sets selected for model validation. We obtained–relative to estimated SEL (Standard Error of Laboratory reference measurements) of 1.07 and 0.97 g kg−1 for OC and IC, respectively–SECV (calibration Standard Error of Cross-Validation) values of 1.04–1.20 and 1.54–1.63 g kg−1, and SEP (validation Standard Error of Prediction) values of 1.09–1.27 and 1.43–1.63 g kg−1. These results, together with validation RPD (Residual Prediction Deviation) values ≥2, could suggest a stable, effective PLS calibration that could be applied to similar soils in the same physiographic region. However, when we attempted to predict soil C for each of the six sites in turn using the remaining five sites for calibration, the models failed completely at two of the six sites and gave inconsistent results at a third site despite pre-screening for spectral similarity. “One-off” local calibrations for this study required ∼20–35% of the full samples, which could be prohibitively expensive for many applications. The results of this study demonstrate that “pseudo-independent” validation (random selection of non-independent test samples) can overestimate predictive accuracy relative to independent validation. The spatial structure of calibration and validation samples matters a great deal. Greater care needs to be taken to ensure that validation samples are independent to a degree that matches intended model use. [Copyright &y& Elsevier]