With the rapid development of digital rural development and the platform economy, short-video platform is becoming an important channel through which farmers obtain agricultural information. Unlike traditional information channels that rely on acquaintance networks, offline extension, and active searching, short-video platforms embed agricultural knowledge into farmers’ daily information exposure through algorithmic recommendation, visual presentation, and continuous content delivery, thereby potentially affecting their farm production and management decisions.

From the perspective of productive use, this study examined the impact of short-video agricultural information adoption on agricultural green production efficiency (AGPE) and further explored the moderating role of digital literacy and the underlying mechanism.

Based on survey data collected in 2023 from maize farmers in the major spring maize-producing areas of Heilongjiang, Jilin, Inner Mongolia, and Hebei, this study obtained 1,286 valid samples. Farmers’ AGPE was measured using an SBM model with undesirable outputs. Short-video agricultural information adoption was defined as whether farmers actually applied agricultural information recommended by short-video platforms to farm production and management decisions, including pest control, fertilization and pesticide use, field management, input procurement, variety selection, and sales pricing. Regression models were used to estimate its effect on AGPE, while moderating-effect and mediating-effect models were employed to test the moderating role of digital literacy and the mediating role of green production behavior. Heterogeneity analysis, propensity score matching, and an instrumental variable approach were further used for robustness tests.

(1) Short-video agricultural information adoption significantly improved farmers’ AGPE, and this finding remained robust after controlling for household head characteristics, family endowments, production and operating conditions, external environmental factors, and provincial fixed effects. (2) Digital literacy exerted a significant positive moderating effect, indicating that the positive effect of short-video agricultural information adoption on AGPE was stronger among farmers with higher levels of digital literacy. (3) Green production behavior played a partial mediating role, with the mediating effect accounting for about 13.0% of the total effect, suggesting that short-video agricultural information adoption improved AGPE partly by promoting the application of green technologies. (4) The promoting effect was relatively stronger among farmers with higher digital literacy and lower levels of off-farm employment, while remaining significantly positive across different farm sizes, indicating a certain degree of inclusiveness. (5) The core findings remained valid after potential endogeneity was addressed using an instrumental variable approach.

Short-video platforms have gradually become an important information channel for farmers to acquire agricultural knowledge and optimize farm production and management decisions. What matters for AGPE is not merely whether farmers are exposed to short videos, but whether they translate platform-recommended agricultural information into actual farm production and management decisions. Short-video agricultural information adoption could significantly improve farmers’ AGPE. Green production behavior was an important pathway through which this effect was realized, while digital literacy was a key condition determining whether platform information could be transformed into efficiency gains.

Enhancing agricultural economic resilience is a critical strategic path for ensuring national food security and promoting the comprehensive implementation of rural revitalization. Against the backdrop of accelerated digital penetration in rural areas, existing research often focuses on macro-level digitalization, making it difficult to isolate the authentic contribution of digital inputs to agricultural systems. The driving effects and internal mechanisms of information and communication technology (ICT) input on agricultural economic resilience are explored in this research. Through clarifying its asymmetric impacts on resistance, recovery, and development capacities, a robust theoretical reference and empirical basis are offered for formulating differentiated digital agriculture policies that transition from traditional production modes to intelligent, resilient systems.

Based on Chinese provincial non-continuous panel data for 2012, 2015, 2017, 2018, and 2020, an advanced input-output (I-O) model framework was utilized. Leveraging the multi-regional input-output tables, the Leontief inverse matrix was employed to calculate the total consumption coefficient of the "Information Transmission, Software, and Information Technology Services" industry by the agricultural sector, which defined the total digital technology input value. Simultaneously, the entropy weight method was used to construct a comprehensive evaluation system for agricultural economic resilience. In terms of the econometric strategy, potential endogeneity was addressed by selecting the product of rural radio stations in 1988 and the previous year's Internet users as an instrumental variable (IV). The analysis was further supported by a 5% bilateral winsorization and a mediation effect model for rigorous empirical testing.

The empirical results demonstrated that digital technology input significantly enhanced overarching agricultural economic resilience. Benchmark regressions showed that the coefficient of ICT input was significantly positive at the 1% level, and the driving effect remained robust after correcting for endogeneity bias, which confirmed the core role of digital transformation in systemic risk management. Dimensional decomposition revealed a significant asymmetric characteristic: Digital technology strongly drives recovery capacity after exogenous shocks and developmental capacity during long-term evolution. However, its impact on the resistance dimension was relatively limited and exhibited a marginal negative effect. This reflected a potential technological dependence risk, where the system's increased sensitivity to power grids and network stability might weaken its original stress-resistance capacity during the onset of extreme risks. Furthermore, control variable analysis showed that per capita gross domestic product (GDP) and optimized planting structures promoted resilience, while the number of rural cooperatives exerted a negative influence, suggesting that some grassroots organizations suffered from insufficient digital adaptability. Mechanism analysis indicated that marketization, economic efficiency, and transport density were the primary transmission paths. Specifically, the marketization path contributed most significantly by reducing institutional transaction costs. Additionally, digital technology improved output efficiency through precision management and optimized transport logistics in synergy with physical infrastructure. Heterogeneity analysis showed that digital technology exhibited a clear "digital compensation" advantage in Western China, effectively offsetting natural resource endowment disadvantages.

This study confirms that digital input constitutes a new quality productive force that fundamentally strengthens the risk-resistance capacity of agricultural systems. The conclusions are summarized as follows: First, the empowerment of agricultural resilience by digital technology is characterized by a profound asymmetry. While it significantly improves the efficiency of systemic recovery and evolutionary development, it may simultaneously weaken original resistance due to intensified technological coupling and infrastructure dependence. Second, the reduction of institutional transaction costs through marketization is identified as the core mechanism for digital factors to exert their resilience-enhancing effects. The depth of the digital dividend is largely determined by the maturity of the market environment and its capacity for factor mobility. Third, the release of digital dividends in agriculture is heavily constrained by organizational adaptability. The lagging digital transformation and inherent structural rigidity of certain grassroots organizations have become the primary institutional bottlenecks restricting the conversion of digital technology inputs into practical systemic resilience. Ultimately, achieving a resilient agricultural economy requires a synergistic alignment between advanced digital production forces and modernized rural production relations.

Developing new-quality productivity is of great significance for promoting high-quality development of animal husbandry. However, there is currently limited research on new-quality productivity in animal husbandry, and there is a lack of in-depth analysis on its connotation, characteristics, constraints, and promotion path.

This article conducts a systematic study on the high-quality development of animal husbandry productivity driven by artificial intelligence. The new-quality productivity of animal husbandry is led by cutting-edge technological innovations such as biotechnology, information technology, and green technology, with digitalization, greening, and ecologicalization as the direction of industrial upgrading. Its basic connotation is manifested as higher quality workers, more advanced labor materials, and a wider range of labor objects. Compared with traditional productivity, the new-quality productivity of animal husbandry is an advanced productivity guided by technological innovation, new development concepts, and centered on the improvement of total factor productivity. It has significant characteristics of high production efficiency, good industrial benefits, and strong sustainable development capabilities. China's new-quality productivity in animal husbandry has a good foundation for development, but it also faces constraints such as insufficient innovation in animal husbandry breeding technology, weak core competitiveness, low mechanization rate of animal husbandry, weak independent research and development capabilities of intelligent equipment, urgent demand for "machine replacement", shortcomings in the quantity and quality of animal husbandry talents, low degree of scale of animal husbandry, and limited level of intelligent management. Artificial intelligence in animal husbandry can be widely used in environmental control, precision feeding, health monitoring and disease prevention and control, supply chain optimization and other fields. Artificial intelligence, through revolutionary breakthroughs in animal husbandry technology represented by digital technology, innovative allocation of productivity factors in animal husbandry linked by data elements, and innovative allocation of productivity factors in animal husbandry adapted to the digital economy, has given birth to new-quality productivity in animal husbandry and empowered the high-quality development of animal husbandry.

This article proposes a path to promote the development of new-quality productivity in animal husbandry by improving the institutional mechanism of artificial intelligence to promote the development of modern animal husbandry industry, strengthening the application of artificial intelligence in animal husbandry technology innovation and promotion, and improving the management level of artificial intelligence in the entire industry chain of animal husbandry.