Jianmin Cao

This study investigates the key factors influencing both the decision to adopt and the level of use of improved teff seed technology among farmers in the Jabitehnan district, Northwest Ethiopia. Despite teff being a vital staple cereal crop extensively cultivated in Ethiopia, it shows inconsistent yields mainly due to a combination of demographic, socioeconomic, and institutional factors. A two‐stage random sampling method was used to gather data from a sample of 384 respondents, of whom 276 (71.87%) were adopters and 108 (28.13%) were nonadopters. Descriptive and inferential statistics, including t ‐tests and chi‐square tests, were employed to characterize the selected households. Furthermore, Cragg's double‐hurdle econometric model was utilized to analyze the determinants affecting household adoption and the intensity of improved teff seed use. The results from the first hurdle (adoption decision) indicate that farming experience, education level, farm size, participation in demonstration plots, attendance at field days, family size, frequency of extension contacts, and household income all have positive and significant effects on the likelihood of adoption. Conversely, the age of the household has a negative effect. The second hurdle (intensity of adoption) results suggest that the level of improved teff seed use is similarly positively influenced by education, farm size, participation in demonstrations and field days, extension contacts, and income, while age remains negatively associated. Based on these findings, researchers, policymakers, extension service providers, and development organizations focused on agriculture should prioritize these key factors to promote the adoption and intensive use of improved teff technologies.

Introduction: Warming caused by greenhouse gas (GHG) emissions has become a global environmental issue of widespread concern, and China, as a responsible power, has the pressing task of reducing carbon emissions. China is one of the world’s major beef producers and consumers, and at the same time, beef cattle, as a large livestock, is the largest source of GHG emissions in the livestock industry.Methods: This study considered the panel data of 31 provinces in China from 2008 to 2022. The kernel density estimation and Dagum Gini coefficient were used to analyze the spatiotemporal dynamic evolution patterns and influencing factors of carbon emissions from China’s beef cattle farming industry.Results: (1) The carbon emission trajectory of beef cattle production follows a distinctive “ascend-descend-ascend” three-phase pattern. By 2022, the sector’s cumulative carbon emissions had burgeoned by 37.62% relative to 2008, reflecting an average annual escalation of 2.31%. Despite the overall upward trend in carbon emissions, significant regional differences were observed. The Central Plains region has witnessed a consistent decline, in stark contrast to the Southwest and Northeast regions, which have emerged as hotspots for heightened carbon emissions and intensified emission densities within China’s beef cattle production landscape, underscoring the intensifying significance of carbon mitigation measures. (2) The kernel density curve shows an overall rightward shift with a specific gradient effect on carbon emissions. In addition, the range of the right drag of the curve in 2022 was significantly reduced, which laterally reflects the narrowing of the difference between the provinces with the highest and lowest carbon emissions from beef cattle farming. The principal source of variance in the overall carbon emissions from beef cattle production is the disparities between regions, which accounts for an average annual contribution rate of 52.52%. Conversely, the within-region contribution rates have remained relatively stable, while those for the intensity of transvariation have witnessed a substantial rise, with annual averages of 18.31 and 28.96%, respectively. (3) Regarding the factors influencing carbon emissions reduction, environmental regulations and production efficiency significantly drive carbon emissions reduction in beef cattle farming.Discussion: Relevant government departments should actively guide farmers toward green production, establish perfect policies and regulations for low-carbon beef cattle farming, and promote low-carbon beef cattle farming models based on local conditions.

The sustainable growth of income for farming households in pastoral areas represents a crucial challenge not only for China but also for other developing nations in the process of transition to ecologically friendly models. The scarcity of assets available for collateral, along with vulnerable production conditions and developmental shortcomings, pose significant barriers to the consistent expansion of herdsmen’s income. This paper analyzes the impact of digital financial inclusion on the income of herdsmen in ethnic minority regions using panel data models from nine major pastoral provinces in China from 2011 to 2022. Through a fixed-effects model approach, the analysis delves into the influence of digital financial inclusion and subjects it to multi-dimensional and robustness tests to ensure the reliability of the findings. Furthermore, the research explores the underlying mechanisms through which digital financial inclusion contributes to income enhancement for herdsmen in these areas, employing mediation effect techniques to provide deeper insights into the relationship between digital financial services and economic upliftment in remote ethnic communities. The research findings indicate that digital financial inclusion has a positive impact on the income of herdsmen in ethnic minority regions. The breadth of coverage, depth of usage, and level of digitalization of digital financial inclusion all play a significant role in enhancing the income levels of herdsmen. In terms of the degree of influence, the impact of digital finance on the income of ethnic minorities follows this order: depth of usage > degree of digitalization > breadth of coverage. Notably, through financial backing that catalyzes a shift in herdsmen’s production methodologies and propels industrial upgrading, there is a profound potential to increase herdsmen’s output efficiency. This, in turn, alleviates the ecological strain on China’s environmentally delicate zones. Moreover, financial backing can condense the breeding periods for herdsmen, thereby ameliorating the ecological degradation associated with excessive grazing in pastoral regions. A mediating-effect analysis reveals that digital financial inclusion can boost economic development in ethnic minority regions by increasing wage income, raising per capita GDP levels, and promoting industrial structure upgrading. This, in turn, leads to an improvement in the income of herdsmen in these regions. This is particularly pertinent in China, where the eco-fragility of pastoral regions coincides with the modest income of herdsmen, underscoring the critical importance of herdsmen’s finance. To foster sustainable progress in these regions, financial backing is imperative to elevate herdsmen’s income and to induce a transformative shift in production approaches and industrial layout, paving the way for the sustainable development of these pastoral regions.

This work estimates agricultural total factor productivity (TFP) change in 44 countries in the Sub-Saharan African (SSA) region for a period of 59 years, (1961–2019). We measure TFP by calculating the Malmquist productivity index (MPI) which decomposes the changes into technological change (techch), efficiency change (effch), scale efficiency change (sech) and pure efficiency change (pech). The determinants of agricultural total productivity changes are identified and their effects on TFP measured. We discover average annual decline of 0.975 in agriculture TFP largely due to a 0.975 technological regression during the study period. Our analysis of the determinants of TFP changes establishes that factors like land policies, good governance, conflict/war and credit to agriculture, influence TFP changes.

We investigate the extent and nature of productivity growth in Sub-Saharan African countries using non-parametric frontier techniques. In this work, we examine agricultural total factor productivity (TFP) change in 44 countries in Sub-Saharan Africa (SSA), covering a period of fifty-nine (59) years (1961–2019). We make use of data drawn from the Food and Agriculture Organization of the United Nations. Due to the non-availability of reliable input price data, the study uses data envelopment analysis (DEA) to derive Malmquist productivity indices. The results demonstrate a decline in agricultural TFP growth in the region from 1961 to 2019. This notwithstanding, a general average growth was seen in some of the conventional inputs measured (land, labour, capital, and fertilizer) which are the main drivers of growth in the region. This finding implies that the increase in agricultural output over the past five decades in SSA is mainly due to an increased use of conventional inputs over time, including land, and not due to an increase in the ratio of output over inputs. As a generally acceptable and preferred indicator of technical and efficiency changes, we use TFP to calculate the Malmquist productivity index (MPI).

Time-varying quadratic programming problems (TVQPPs) subject to equality and inequality constraints with different measurement noises often arise in the fields of scientific and economic research. Noises are always ubiquitous and unavoidable in practical problems. However, most existing methods usually assume that the computing process is free of measurement noise or the denoising has been monitored before the calculation. The zeroing neural network (ZNN), a significant neurodynamic approach, has presented potent abilities to compute a great variety of time-varying zeroing problems with odd monotonically increasing activation functions, but the existing results on ZNN cannot deal with the inequality constraints in the TVQPPs. In this article, a nonconvex function activated implicit ZNN (NIZNN) model and a noise-suppressing implicit ZNN (NNSIZNN) model, which is also known as the ZNN-based model, are proposed by the inspiration of the conventional ZNN model from a control-based framework. It guarantees an effective solution for TVQPPs in the presence of different noises. The ZNN-based model allows nonconvex sets for projection operation in activation functions and incorporates noise-tolerant techniques for handling different noises arising in TVQPPs. Besides, theoretical analyses show that the ZNN-based model globally converges to the time-varying optimization solution of TVQPPs in the presence of noises. In addition, an illustrative example is provided to substantiate the efficiency and robustness of the ZNN-based model for online solving TVQPPs with inherent tolerance to noises. Moreover, the ZNN-based model is applied to the economic model, which provides and investigates their computational efficiency and superiority. Finally, an application example to repetitive motion generation of four-wheel omnidirectional mobile robot is simulated to substantiate the feasibility and superiority of the developed NNSIZNN model for online solving TVQPPs with nonconvex constraints and measurement noise.