Digital technology dilemma: on unlocking the soil quality index conundrum

A Longitudinal Analysis of the Impact of Digital Technologies on Sustainable Food Production and Consumption in the European Union

In today's landscape, digital technologies hold immense potential in tackling challenges associated with food sustainability. This study aims to contextualize a broader investigation of food sustainability and digitalization within the agricultural sector. Its objective is to explore the influence of digital technologies on sustainable food production and consumption, particularly examining relationships among digital technologies, municipal waste, agricultural output, nitrogen emissions, methane emissions from agriculture, and Goal 12 Responsible Consumption and Production (SDG12). Through the use of Structural Equation Modeling, the empirical investigation scrutinizes the relationships between digital technology use and critical variables linked to food sustainability in a longitudinal analysis. The results highlight the significant impact of extensive digital technology use on municipal waste, sustainable production, and consumption, indirectly influencing greenhouse gas (GHG) emissions. Empirical research findings reveal a negative influence of digital technologies on responsible consumption and production (path coefficient -0.349, p values < 0.001), suggesting an impact of digital technologies on diminishing sustainability in consumption and production. The relationship between digital technologies and municipal solid waste is also negative (path coefficient -0.360, p values < 0.001), suggesting that the use of digital technologies can contribute to reducing the amount of municipal solid waste. Digitalization has the potential to improve the sustainability of supply chains by reducing resource consumption and greenhouse gas emissions associated with production and distribution operations.

Does digital technology innovation promote low-carbon development in agriculture?: A spatial econometric analysis based on 31 provinces in China

China's traditional agriculture has grown quickly but at the cost of excessive pollution and energy consumption. Therefore, low-carbon development in agriculture is crucial to achieving "carbon neutrality" and "carbon peaking." With the development of China's digital economy and the construction of digital villages in recent years, digital technology innovation (DTI) is probably going to a significant role in lowering agricultural carbon emissions (ACEs). Based on Chinese provincial panel data from 2006 to 2021, we analyze the spatial and temporal evolution characteristics of DTI and ACE, explore the impact and the pathways of DTI on ACE using a spatial econometric model, and reveal this impact's heterogeneity and nonlinear character. The findings show that DTI and ACE increased significantly throughout all Chinese provinces. DTI agglomeration is becoming stronger, whereas ACE displays a tendency for discontinuous distribution. DTI is essential in promoting low-carbon development in agriculture, and there are significant spatial spillover effects due to technology spillovers. Heterogeneity analysis indicates that DTI has positive impacts on local agriculture in different regions. However, there is variation in the impact's degree. The mechanism test's findings show how DTI reduces ACE by improving technology and enhancing resource endowment. The relationship between DTI and ACE exhibits an inverted "U" curve, and the level of economic development is the threshold variable that constrains the relationship between the two variables. To achieve a regional balanced low-carbon development in agriculture through DTI, it is important to emphasize the impact of DTI on reducing carbon emissions and to encourage the transfer of mature technology from high-level regions to low-level regions.

AI and machine learning for soil analysis: an assessment of sustainable agricultural practices

Sustainable agricultural practices help to manage and use natural resources efficiently. Due to global climate and geospatial land design, soil texture, soil-water content (SWC), and other parameters vary greatly; thus, real time, robust, and accurate soil analytical measurements are difficult to be developed. Conventional statistical analysis tools take longer to analyze and interpret data, which may have delayed a crucial decision. Therefore, this review paper is presented to develop the researcher's insight toward robust, accurate, and quick soil analysis using artificial intelligence (AI), deep learning (DL), and machine learning (ML) platforms to attain robustness in SWC and soil texture analysis. Machine learning algorithms, such as random forests, support vector machines, and neural networks, can be employed to develop predictive models based on available soil data and auxiliary environmental variables. Geostatistical techniques, including kriging and co-kriging, help interpolate and extrapolate soil property values to unsampled locations, improving the spatial representation of the data set. The false positivity in SWC results and bugs in advanced detection techniques are also evaluated, which may lead to wrong agricultural practices. Moreover, the advantages of AI data processing over general statistical analysis for robust and noise-free results have also been discussed in light of smart irrigation technologies. Conclusively, the conventional statistical tools for SWCs and soil texture analysis are not enough to practice and manage ergonomic land management. The broader geospatial non-numeric data are more suitable for AI processing that may soon help soil scientists develop a global SWC database.