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Abstract Natural climate solutions provide opportunities to reduce greenhouse gas emissions and the United States is among a growing number of countries promoting storage of carbon in agricultural soils as part of the climate solution. Historical patterns of soil organic carbon (SOC) stock changes provide context about mitigation potential. Therefore, our objective was to quantify the influence of climate-smart soil practices on SOC stock changes in the top 30 cm of mineral soils for croplands in the United States using the DayCent Ecosystem Model. We estimated that SOC stocks increased annually in US croplands from 1995 to 2015, with the largest increase in 1996 of 16.6 Mt C (95% confidence interval ranging from 6.1 to 28.2 Mt CO2 eq.) and the lowest increase in 2015 of 10.6 Mt C (95% confidence interval ranging from − 1.8 to 22.2 Mt C). Most climate-smart soil practices contributed to increases in SOC stocks except for winter cover crops, which had a negligible impact due to a relatively small area with cover crop adoption. Our study suggests that there is potential for enhancing C sinks in cropland soils of the United States although some of the potential has been realized due to past adoption of climate-smart soil practices.

This paper presents a new regression estimator for the total of a population created by means of the minimization of a measure of dispersion and the use of the Wilcoxon scores. The use of a particular nonparametric model is considered in order to obtain a model-assisted estimator by means of the generalized difference estimator. First, an estimator of the vector of the regression coefficients for the finite population is presented and then, using the generalized difference principles, an estimator for the total a population is proposed. The study of the accuracy and efficiency measures, such as design bias and mean square error of the estimators, is carried out through simulation experiments.

Studies examining the value of switching to a variable rate technology (VRT) fertilizer program assume producers possess perfect soil nitrate information. In reality, producers estimate soil nitrate levels with soil sampling. The value of switching to a VRT program depends on the quality of the estimates and on how the estimates are used. Larger samples sizes, increased spatial correlation, and decreased variability improve the estimates and increase returns. Fertilizing strictly to the estimated field map fails to account for estimation risk. Returns increase if the soil sample information is used in a Bayesian fashion to update the soil nitrate beliefs in nonsampled sites.

Understanding sensitive behaviors-those that are socially unacceptable or non-compliant with rules or regulations-is essential for creating effective interventions. Sensitive behaviors are challenging to study, because participants are unlikely to disclose sensitive behaviors for fear of retribution or due to social undesirability. Methods for studying sensitive behavior include randomized response techniques, which provide anonymity to interviewees who answer sensitive questions. A variation on this approach, the quantitative randomized response technique (QRRT), allows researchers to estimate the frequency or quantity of sensitive behaviors. However, to date no studies have used QRRT to identify potential drivers of non-compliant behavior because regression methodology has not been developed for the nonnegative count data produced by QRRT. We develop a Poisson regression methodology for QRRT data, based on maximum likelihood estimation computed via the expectation-maximization (EM) algorithm. The methodology can be implemented with relatively minor modification of existing software for generalized linear models. We derive the Fisher information matrix in this setting and use it to obtain the asymptotic variance-covariance matrix of the regression parameter estimates. Simulation results demonstrate the quality of the asymptotic approximations. The method is illustrated with a case study examining potential drivers of non-compliance with hunting regulations in Sierra Leone. The new methodology allows assessment of the importance of potential drivers of different quantities of non-compliant behavior, using a likelihood-based, information-theoretic approach. Free, open-source software is provided to support QRRT regression.

Developing countries face many challenges when constructing national inventories of greenhouse gas (GHG) emissions, such as lack of activity data, insufficient measurements for deriving country-specific emission factors, and a limited basis for assessing GHG mitigation options. Emissions from agricultural production are often significant sources in developing countries, particularly soil nitrous oxide, and livestock enteric and manure methane, in addition to wetland rice methane. Consequently, estimating GHG emissions from agriculture is an important part of constructing developing country inventories. While the challenges may seem insurmountable, there are ways forward such as: (a) efficiently using resources to compile activity data by combining censuses and surveys; (b) using a tiered approach to measure emissions at appropriately selected sites, coupled with modeling to derive country-specific emission factors; and (c) using advanced software systems to guide compilers through the inventory process. With a concerted effort by compilers and assistance through capacity-building efforts, developing country compilers could produce transparent, accurate, complete, consistent and comparable inventories, as recommended by the IPCC (Intergovernmental Panel on Climate Change). In turn, the resulting inventories would provide the foundation for robust GHG mitigation analyses and allow for the development of nationally appropriate mitigation actions and low emission development strategies.