Climate Smart Agriculture最新文献

Soil organic carbon (SOC) dynamics under elevated atmospheric CO₂ concentration has been widely reported, however, in which the behaviors of active and passive fractions remain inadequately explored. Here we studied this issue using three pairs of active and passive fractions of SOC under a 10-year free-air CO₂ enrichment experiment (550 ​± ​17 ​ppm) in a cropland in the North China Plain. We found that decadal elevated CO₂ increased the root biomass, root exudation rate and microbial biomass, but had little effects on SOC pool size. Elevated CO₂ increased the readily oxidizable organic carbon (ROOC) and particulate organic carbon (POC) due to the increments of root C input, but decreased their paired passive fractions possibly because of the carbon input-induced positive priming effect. Our results indicate the reduced stability of SOC pool under elevated CO₂. This is significant for better predicting SOC feedback to future climate change.

To mitigate the impact of climate change, farmers are increasingly opting for more efficient energy allocation in agricultural production. This study aims to evaluate the effectiveness of these methods employed by maize growers in Benin, while identifying the constraints associated with their implementation. A survey was conducted among 230 maize growers in Benin to achieve the objectives of the study. The Data Envelopment Analysis method was utilized to measure farmers' technical efficiency, followed by the application of the Tobit model to identify the factors determining this efficiency. The comparative analysis of efficiency indices reveals that farmers who prioritize increased utilization of agricultural inputs exhibit higher levels of technical efficiency while maintaining constant yields. In terms of technical efficiency at varying yields, farmers who increase their labor input demonstrate the highest level of efficiency. Subsequently, farmers who choose to augment the quantities of agricultural inputs exhibit greater scale efficiency. The Tobit model reveals that age, experience, maize production area, utilization of insecticides and NPK fertilizers are significant determinants influencing the efficiency levels of maize growers. Maize growers encounter challenges in accessing improved maize seeds and agricultural machinery, as well as facing financial constraints.

In the Everglades Agricultural Area (EAA), Florida, cultivating rice in flooded paddies is becoming increasingly popular to conserve water and soil health. Flood depth is a critical factor affecting the discharged water quality, soil carbon, and yield production. However, few studies have comprehensively investigated the optimal flood depth in EAA, considering multi-functional indices. To address this gap, we investigated drainage water quality, water quantity, nutrient uptake, soil carbon, and rice yield in rice paddies in histosol soils over a two-year period at four flood depths (5, 10, 15, and 20 ​cm). For each flood depth, averaged over two years, total outflow loadings of suspended solids, nitrogen, phosphorus, and potassium were significantly reduced by 40 ​%, 38 ​%, 36 ​%, and 32 ​%, respectively, compared to inflow water loadings (p ​< ​0.001). Total phosphorus uptake averaged ∼11.21 kg ha⁻¹ in rice shoots and 0.48 kg ha⁻¹ in roots, while total potassium uptake averaged ∼4.28 kg ha⁻¹ in shoots and 0.13 kg ha⁻¹ in roots. Soil organic carbon (SOC) in 5, 10, 15, and 20 ​cm flood treatments increased annually at a rate of 3.85 ​%, 5.64 ​%, 6.86 ​%, and 6.86 ​%, respectively; for these same treatments, soil active organic carbon (AOC) decreased annually at rates of 11.75 ​%, 8.63 ​%, 20.07 ​%, and 8.48 ​%, and rice grain yield was 4488, 5103, 5450, and 5386 ​kg ​ha⁻¹, respectively. Overall, considering the water quality, SOC, AOC, and rice yield production, irrigating rice paddies at a flood depth of 15 ​cm most effectively improves water quality, increases carbon sequestration, reduces active carbon, and yields more rice than other flood depths. By evaluating the effects of flood depth on the soil–water–plant nexus in a holistic manner, we propose a more sustainable and environmentally friendly mode of rice cultivation within the EAA.

This study examined the resilience to climate change of smallholder family farms in the Centre Region of Cameroon. Data were collected using a mixed-methods strategy and analyzed using descriptive, multivariate, and inferential statistics. Family farms exhibited a mean climate resilience index of 0.46 (medium), with the Ntui, Mbangassina, Batchenga, and Obala regions scoring 0.42, 0.44, 0.47, and 0.51, respectively. Family farmers had a high transformation capacity (59.07 ​%), a low adaptation capacity (32.10 ​%), and a very low absorption capacity (8.82 ​%). Logistic regression revealed significant causal relationships (p ​< ​0.05) between the capacity of the farms to adapt to climate fluctuations and change and annual income, access to agricultural inputs, access to agricultural machinery, and membership in a farmers organization. These are the primary factors that could significantly increase climate resilience in Cameroonian family farms. Consequently, policymakers in these regions and beyond should consider these as indicators when developing policies to strengthen the climate resilience of local agricultural systems. In doing so, they should also consider community monitoring and indigenous knowledge, which can help bridge the gap between local adverse impacts and the necessary adaptations to climate change.

Agriculture, broadly defined to include crop and livestock production, forestry, aquaculture and fishery, represents a key source or sink of greenhouse gas emissions. It is also a vulnerable sector under climate change. The term climate-smart agriculture has been widely used since its inception in 2010, but no clear and unified understanding of its scientific meaning exists. Here, we systematically analyzed the relationship between agriculture and climate change and interpreted the scientific definition of climate-smart agriculture. We believe that climate-smart agriculture represents a modern production approach to coordinatively promote food security, climate mitigation benefits and agricultural adaptation to climate change towards the Sustainable Development Goals. In addition, due to the worsening global climate change situation, we expounded on the urgency and major challenges in promoting climate-smart agriculture.

Modeling soil organic carbon (SOC) is helpful for understanding its distribution and turnover processes, which can guide the implementation of effective measures for carbon (C) sequestration and enhance land productivity. Process-based simulation with high interpretability and extrapolation, and machine learning modeling with high flexibility are two common methods for investigating SOC distribution and turnover. To take advantage of both methods, we developed a hybrid model by coupling of a two-carbon pool microbial model and machine learning for SOC modeling. Here, we assessed the SOC model's predictive, mapping, and interpretability capabilities for the SOC turnover process on Ningbo region. The results indicate that the microbial model with density-dependence (β ​= ​2) and microbial biomass carbon simulation performed better in modeling the parameters of the microbial-based C cycle, such as microbial carbon use efficiency (CUE), microbial mortality rate, and assimilation rate. By integrating this optimal microbial model and random forest (RF) model, the hybrid model improved the prediction accuracy of SOC, with an increased R² from 0.74 to 0.84, residual prediction deviation increased from 1.97 to 2.50, and reduced the root-mean-square error from 4.65 to 3.67 ​g ​kg⁻¹ compared to the conventional RF model. As a result, the predicted SOC distribution exhibited high spatial variation and provided abundant details. Microbial CUE and potential C input, represented by net primary productivity, emerged as the primary factors driving SOC distribution in Ningbo region. Projections of SOC under the CMIP6 SSP2-4.5 scenario revealed that regional C loss in high SOC areas was mainly caused by decreased microbial CUE and C input, induced by climate change. Our findings highlight the potential of combining the microbial-explicit model and machine learning to improve SOC prediction accuracy and understand SOC feedback in a changing climate.