The agricultural industry is getting more data-centric and requires precise, more advanced data and technologies than before, despite being familiar with agricultural processes. The agriculture industry is being advanced by various information and advanced communication technologies, such as the Internet of Things (IoT). The rapid emergence of these advanced technologies has restructured almost all other industries, as well as advanced agriculture, which has shifted the industry from a statistical approach to a quantitative one. This radical change has shaken existing farming techniques and produced the latest prospects in a series of challenges. This comprehensive review article enlightens the potential of the IoT in the advancement of agriculture and the challenges faced when combining these advanced technologies with conventional agricultural systems. A brief analysis of these advanced technologies with sensors is presented in advanced agricultural applications. Numerous sensors that can be implemented for specific agricultural practices require best management practices (e.g., land preparation, irrigation systems, insect, and disease management). This review includes the integration of all suitable techniques, from sowing to harvesting, packaging, transportation, and advanced technologies available for farmers throughout the cropping system. Besides, this review article highlights the utilization of other tools such as unmanned aerial vehicles (UAVs) for crop monitoring and other beneficiary measures, such as optimizing crop yields. In addition, advanced programs based on the IoT are also discussed. Finally, based on our comprehensive review, we identified advanced prospects regarding the IoT, which are essential tools for sustainable agriculture.

The field of entrepreneurship is considered essential for the economy, and many researchers around the world have studied it from diverse perspectives. The outcomes of this research are not yet consensual. Today, it is gaining attention and consensus due to the increasing pressure of sustainable development, so there is a need for academic research to examine this field by incorporating sustainability value creation practices and the efforts of current entrepreneurs towards said goal, especially in the case of the agricultural sector. Accordingly, this study aims to address the problem of what drives students to incorporate sustainable agriculture in their entrepreneurial ventures. Moreover, we aim to determine whether the value placed in the triple bottom line affects the intention to establish sustainable businesses. The study is based on five antecedents of the Theory of Planned Behavior (TBP) and was designed to explore the mechanism underlying the intention to promote sustainable entrepreneurship in agriculture. The primary objective was to collect and analyze the data using the partial least square structural equation model (PLS-SEM) to test the determinants. The results revealed that the indicators of a favorable sustainable attitude, supportive subjective norms, control behaviors, adequate opportunity recognitions, and encouraging the triple bottom line had strong influences on the intention of promoting sustainability in entrepreneurship. Besides, the attitudes, subjective norms, opportunity recognition, and sustainability values can also predict students’ significant positive intentions toward sustainable agriculture in entrepreneurship. The research findings contribute to the literature by providing an empirical basis for the formulation of policies to encourage students to start an agribusiness, thereby improving the effectiveness of entrepreneurship education development programs and bridging the gap between sustainable entrepreneurial intentions and actions. Therefore, the insight into the determinants of sustainable entrepreneurship can be an essential step toward designing a practical and durable policy mechanism for the implementation of the triple bottom line when developing entrepreneurial agriculture education programs.

The current study examines the long-run effects of climatic factors on wheat production in China’s top three wheat-producing provinces (Hebei, Henan, and Shandong). The data set consists of observations from 1992 to 2020 on which several techniques, namely, fully modified OLS (FMOLS), dynamic OLS (DOLS), and canonical co-integrating regression (CCR) estimators, and Granger causality, are applied. The results reveal that climatic factors, such as temperature and rainfall, negatively influenced wheat production in Henan Province. This means that Henan Province is more vulnerable to climate change. In contrast, it is observed that climatic conditions (via temperature and rainfall) positively contributed to wheat production in Hebei Province. Moreover, temperature negatively influenced wheat production in Shandong Province, while rainfall contributed positively to wheat production. Further, the results of Granger causality reveal that climatic factors and other determinants significantly influenced wheat production in the selected provinces.