Carbon Farming: Prospects and Challenges

How to reduce the carbon footprint of the agricultural sector? Factors influencing farmers' decision to participate in carbon sequestration programs

Mitigating climate change by sequestering carbon in agricultural soils through humus formation is a crucial component of sustainable agriculture. Humus programs that are designed to incentivize farmers to build more humus are still recent innovations, so current knowledge about farmers' motivation to participate is limited. This study examines the adoption of non-governmental humus programs to promote carbon sequestration by analyzing farmers' willingness to participate in humus programs and influential factors. We specifically investigate behavioral factors underlying farmers' adoption of humus programs using the Theory of Planned Behavior. To this end, we collected data using an online survey with 190 German farmers and applied partial least squares structural equation modeling. The results show that (i) perceived economic benefits, (ii) the actions of fellow farmers, and (iii) farmers' sense of responsibility with regard to climate change have a statistically significant influence on farmers' intention to participate in a humus program. In contrast, the perceived ecological benefits, political pressure, the possibility of establishing humus-building measures, and prior knowledge of humus programs have no statistically significant influence. Our findings suggest that farmers' decision to participate in humus programs is strongly influenced by the financial benefits, but the actions and thoughts of other farmers, as well as their own moral claims regarding climate change, also play a crucial role. We found that farmers lack knowledge about the registration and general functioning of humus programs, which can currently be one of the biggest barriers to participation in such initiatives.

Farmers' Willingness to Participate in a Carbon Sequestration Program - A Discrete Choice Experiment

Farmers can counteract global warming by drawing carbon dioxide from the air into agricultural soils by building up humus. Humus programs were developed to motivate farmers for even more humus formation (= carbon sequestration) through an additional financial incentive. These programs are still at an early stage of development, which is why the number of participating farmers and research work is still low. This study is the first to analyze the willingness of German farmers to participate in hypothetical humus programs. The results of a discrete choice experiment show that a (higher) threshold for the payout of the premium, regional (rather than field-specific) reference values, and the risk of repayment clearly discourage farmers from participating. Program providers must more than double the premium (set at around 240 € per hectare and 0.1% humus increase) to maintain farmers' willingness to participate despite a payout threshold. Regional reference values and an additional premium/repayment system would lead to an increase in the premium of around 20 € per hectare in order to keep the willingness to participate at the same level. The motivation to build up humus, the desire to maximize subsidies, and a higher livestock density have a positive influence on farmers' decision to participate. Farm size and risk attitude have an impact on farmers' preferences for program design. The study is relevant for policymakers and non-governmental organizations concerned with carbon management, as our findings highlight pathways for efficient, targeted designs of humus programs and carbon sequestration policies.

Incentive mechanisms of carbon farming contracts: A systematic mapping study

Despite increasing interest, a lack of comprehensive knowledge regarding the efficient design and implementation of carbon farming schemes remains. These schemes must efficiently achieve higher carbon sequestration, incentivize farmers, and increase farmers' participation in global carbon markets. Our study systematically reviews, describes, and maps available evidence related to carbon farming contracts to assess different incentive mechanisms for carbon farming. We conduct a systematic mapping review of articles extracted from various databases employing the Collaboration for Environmental Evidence method. We shortlist 52 articles and analyze about 40 global case studies, identifying three main incentive mechanisms of carbon farming contracts, namely, result-based, action-based, and hybrid payments. We examine how these incentive mechanisms are designed, in addition to associated payment types, monitoring approaches, and barriers to implementation. Result-based payments include stringent monitoring and can be implemented through auctions, carbon credits, product labels or certificates. Action-based payments are found to be simpler, with lower monitoring requirements for farmers and can be paid upfront or after contract implementation. Hybrid payments combine both techniques, offering low-risk and guaranteed payments for farmers and definite environmental mitigation impacts. Result-based and hybrid payments motivate farmers to innovate to meet environmental objectives while also connecting them to carbon markets. The major challenges to developing a successful carbon farming project include lack of permanence, non-additionality, and the absence of stringent monitoring, reporting, and verification standards, all of which affect farmers' incentives. This study determines that carbon farming contract design and efficiency can be improved by analyzing the lessons learned from previous experiences. By examining and improving the attributes that define different incentive mechanisms, farmers can be better motivated to enroll in carbon farming schemes and benefit from increased access to carbon markets to potentially transform agriculture into a viable tool for climate action.

An Analysis of the Spatiotemporal Characteristics and Diversity of Grain Production Resource Utilization Efficiency under the Constraint of Carbon Emissions: Evidence from Major Grain-Producing Areas in China

As the most important driving force for ensuring the effective supply of grain in the country, the production stability of the major grain-producing areas directly concerns the national security of China. In this paper, considering the “water–soil–energy–carbon” correlation, water, soil and energy resource factors, and carbon emission constraints were included in an index system, and the global common frontier boundary three-stage super-efficient EBM–GML model was used to measure the grain production resource utilization efficiency of the major grain-producing areas in China from 2000 to 2019. This paper also analyzed the static and dynamic spatiotemporal characteristics and the restrictions of utilization efficiency. The results showed that, under the measurement of the traditional data envelopment analysis model, the grain production resource utilization efficiency in the major producing areas is relatively high, but there is still room to improve by more than 20%, and grain production still has enormous growth potential. After excluding external environmental and random factors, it was found that the utilization efficiency of grain production resources in the major producing areas decreased, and the efficiency and ranking of provinces changed significantly. External factors inhibit pure technical efficiency and expand the scale efficiency. The utilization efficiency of Northeast China was much higher than that of the Huang-Huai-Hai region and the middle and upper reaches of the Yangtze River region, and its grain production resource allocation management had obvious advantages. The total factor productivity index of food production resources showed an upward trend as a whole, and its change was affected by both technological efficiency and technological progress, of which technological progress had the greater impact. Therefore, reducing the differences in the external environment of different regions while making adjustments in accordance with their own potential is an effective way to further improve the utilization efficiency of food production resources.

Asymmetric Effects of Economic Development, Agroforestry Development, Energy Consumption, and Population Size on CO2 Emissions in China

The COVID-19 epidemic and the Russian–Ukrainian conflict have led to a global food and energy crisis, making the world aware of the importance of agroforestry development for a country. Modern agriculture mechanization leads to massive energy consumption and increased CO2 emissions. At the same time, China is facing serious demographic problems and a lack of consumption in the domestic market. The Chinese government is faced with the dilemma of balancing environmental protection with economic development in the context of the “double carbon” strategy. This article uses annual World Bank statistics from 1990 to 2020 to study the asymmetric relationships between agroforestry development, energy consumption, population size, and economic development on CO2 emissions in China using the partial least squares path model (PLS-PM), the autoregressive VAR vector time series model, and the Granger causality test. The results are as follows: (1) The relationship between economic development and carbon dioxide emissions, agroforestry development and carbon dioxide emissions, energy consumption and carbon dioxide emissions, and population size and carbon dioxide emissions are both direct and indirect, with an overall significant positive effect. There is a direct negative relationship between population size and carbon dioxide emissions. (2) The results of the Granger causality test show that economic development, energy consumption, and CO2 emissions are the causes of the development of agroforestry; economic development, agroforestry development, population size, and CO2 emissions are the causes of energy consumption; energy consumption is the cause of economic development and CO2 emissions; and agroforestry development is the cause of population size and energy consumption. (3) In the next three years, China’s agroforestry development will be influenced by the impulse response of economic development, energy consumption, and CO2 emission factors, showing a decreasing development trend. China’s energy consumption will be influenced by the impulse response of economic development, agroforestry development, population size, and CO2 emission factors, showing a decreasing development trend, followed by an increasing development trend. China’s CO2 emission will be influenced by the impulse response of energy consumption and agroforestry development. China’s CO2 emissions will be influenced by the impulse response of energy consumption and agroforestry development factors, showing a downward and then an upward development trend.

Scale-Specific Prediction of Topsoil Organic Carbon Contents Using Terrain Attributes and SCMaP Soil Reflectance Composites

There is a growing need for an area-wide knowledge of SOC contents in agricultural soils at the field scale for food security and monitoring long-term changes related to soil health and climate change. In Germany, SOC maps are mostly available with a spatial resolution of 250 m to 1 km2. The nationwide availability of both digital elevation models at various spatial resolutions and multi-temporal satellite imagery enables the derivation of multi-scale terrain attributes and (here: Landsat-based) multi-temporal soil reflectance composites (SRC) as explanatory variables. In the example of a Bavarian test of about 8000 km2, relations between 220 SOC content samples as well as different aggregation levels of the explanatory variables were analyzed for their scale-specific predictive power. The aggregation levels were generated by applying a region-growing segmentation procedure, and the SOC content prediction was realized by the Random Forest algorithm. In doing so, established approaches of (geographic) object-based image analysis (GEOBIA) and machine learning were combined. The modeling results revealed scale-specific differences. Compared to terrain attributes, the use of SRC parameters leads to a significant model improvement at field-related scale levels. The joint use of both terrain attributes and SRC parameters resulted in further model improvements. The best modeling variant is characterized by an accuracy of R2 = 0.84 and RMSE = 1.99.