CORTEXA
← Browse
openalexAgriculture2026-07-23Cited by 0

Does the County-Level Business Environment Improve the Quality of Agriculture-Related Market Entry? Evidence from China

Xingyuan Yao, Jingyi Huang, Yufen Zhong, Qingfan Lin, Weiming Lin

Improving the quality of agriculture-related market entry is central to rural industrial upgrading, yet existing studies mainly measure entry quantity. The primary objective of this study is to assess whether the county-level business environment is associated with the structural quality of new agriculture-related entrants. Specifically, the study asks three questions: whether the association is positive; whether financial services, market connectivity, and human capital supply are consistent transmission channels; and whether the association varies with agricultural foundations, initial factor market conditions, and access to urban spillovers. Using Chinese business registration records and county-level socioeconomic data for 2010–2023, we construct a county–year panel and measure entry quality through capital quality, organizational quality, and value chain extension. Two-way fixed effects, double machine learning, fractional response models, robustness tests, and a supplementary instrumental variable diagnostic are applied. The preferred fixed effects estimate indicates that a 0.1 increase in the business environment index is associated with a 4.16 percentage point increase in the high-quality entry ratio; a one-standard-deviation increase corresponds to approximately 3.77 percentage points. The association remains positive under nonlinear and bounded-outcome specifications. Channel tests are consistent with financial, market connectivity, and human capital pathways, and the association is stronger in central and western counties, non-municipal districts, counties with stronger agricultural foundations, and counties with weaker initial financial and market conditions. Because the estimate becomes statistically insignificant after prefecture-by-year fixed effects are included, the findings are interpreted as robust conditional associations rather than definitive county-level causal effects.

View free PDFSource page

Related papers

openalexAgriculture2026-07-24

Recognition of Posture Transition Behavior in Sows Approaching Parturition Based on YOLOv11 and a Multi-Scale RGB–Flow Cross-Modal Temporal Network

Runhe Xue, Rui Ye, Yingjun Xiong, Yu Ding

Posture transition behavior in sows approaching parturition provides an important physiological cue for farrowing prediction. However, manual monitoring is time-consuming, labor-intensive and difficult to sustain under nighttime production conditions, while existing machine visio…

View free PDFSource page
openalexAgriculture2026-07-23

Integrating GIS-MCDA and Machine Learning Approach to Identify Marginal and Underutilized Lands in Leon County, Florida

Tewodros A. Simret, Sewunet A. Natae, Gang Chen, Victor Ibeanusi, Hubert Hirwa

This study developed an integrated Geographical Information System (GIS) based Multi-Criteria Decision Analysis (GIS-MCDA) framework based on hydrological marginality, soil suitability, land cover marginality, slope, social marginality, and contaminated land indicators to identif…

View free PDFSource page
crossrefAgriculture2026-07-23

UAV Multispectral Estimation of Citrus Leaf Nitrogen Content by Integrating Object-Based Canopy Extraction and PSO-Optimized Machine Learning

Hongmei Gu, Weiqi Zhang, Yuliang Fu, Yun Zhong, Songlin Wang

Leaf nitrogen content (LNC) is an important physiological indicator for evaluating citrus nutritional status, photosynthetic capacity, and fertilization demand. However, conventional LNC determination mainly relies on field sampling and laboratory chemical analysis, which are des…

View free PDFSource page
crossrefAgriculture2026-07-19

An Operation-Centered Review of Deep-Learning Computer Vision for Dairy Cow Management

Chi Zhang, Ziwen Yu, Jin Wang

Dairy cow management depends on repeated observations of behavior and physical condition to support health, welfare, and operational decisions, but these observations remain labor-intensive. Deep learning (DL)-based computer vision can automate parts of this work, although deploy…

View free PDFSource page
crossrefAgriculture2026-06-10

Unveiling the Effects of Digital Transformation on Agribusiness Green Innovation in China: An Explainable Machine Learning-Based Approach

Wanqi Liang, Xin Feng

Digital transformation is a key driver of green innovation in agribusiness. While the positive impact of digital transformation on firm innovation has been well documented, its multidimensional nature and heterogeneous associations on agribusiness green innovation remain underexp…

View free PDFSource page
crossrefAgriculture2026-06-03

Digital Soil Mapping of the Steppe Zone in Northern Kazakhstan: Predicting Agrochemical Properties of Soils Using Multimodal Satellite Data and Machine and Deep Learning Techniques

Aliya Yskak, Gulnaz T. Yermoldina, Almabek B. Nugmanov, Berik S. Rakhimbayev, Zhanna B. Suimenbayeva, Vladimir D. Fominov, et al.

Digital soil mapping (DSM), based on multimodal satellite data, is a crucial tool for the transition to precision agriculture. However, systematic studies using this method and machine and deep learning techniques are lacking for the arid and semi-arid regions of Central Asia, wh…

View free PDFSource page