Boundary-spanning search for knowledge is an effective way for enterprises to acquire heterogeneous knowledge, and is also an important pre-stage to realize effective knowledge reconstruction. Based on the boundary-spanning search for knowledge theory, this paper studies the relationship between boundary-spanning search for knowledge and the sustainable innovation ability of agricultural enterprises considering the influence of organizational knowledge reconstruction, from a Chinese perspective.
Agriculture is an essential component of food security, sustainable livelihoods, and economic development in sub-Saharan Africa (SSA). Smallholder farmers, however, are restricted in the number of crops they can grow due to small plot sizes. Agriculture inputs, such as fertilizers, herbicides or pesticides, and improved seed varieties, could prove to be useful resources to improve yield. Despite the potential of these agriculture technologies, input use throughout much of SSA remains low.
The use of technology in agriculture plays an important role in the production chain cycle, as well as in the improvement of processes and productivity. To develop a model for measuring the technological capacity of family agriculture systems, it is necessary to assess the gaps related to indicators and the technological potentialities of these farmer groups, which are often not considered when they require financial support and do not get enough. Thus, the aim of this study is to identify the indicators used to evaluate the technological capacity of farm systems and agriculture.
The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse.
The impact of global warming on crop growth periods and yields has been evaluated by using crop models, which need to provide various kinds of input datasets and estimate numerous parameters before simulation. Direct studies on the changes of climatic factors on the observed crop growth and yield could provide a more simple and intuitive way for assessing the impact of climate change on crop production.
Soil texture is a key soil property influencing many agronomic practices including fertilization and liming. Therefore, an accurate estimation of soil texture is essential for adopting sustainable soil management practices. In this study, we used different machine learning algorithms trained on vis–NIR spectra from existing soil spectral libraries (ICRAF and LUCAS) to predict soil textural fractions (sand–silt–clay %). In addition, we predicted the soil textural groups (G1: Fine, G2: Medium, and G3: Coarse) using routine chemical characteristics as auxiliary.
Visible and near-infrared diffuse reflectance spectroscopy (VIS-NIR) has shown levels of accuracy comparable to conventional laboratory methods for estimating soil properties. Soil chemical and physical properties have been predicted by reflectance spectroscopy successfully on subtropical and temperate soils, whereas soils from tropical agro-forest regions have received less attention, especially those from tropical rainforests. A spectral characterization provides a proficient pathway for soil characterization.
This research examines the transformation of the agro-climatic conditions of the Altai region as a result of climate change. The climate of the Altai region in Russia is sharply continental and characterized by dry air and significant weather variability, both in individual seasons and years. The current study is determined by the lack of detailed area-related analytical generalizations for the territory of the Altai region over the past 30 years.
Recent Society 5.0 efforts by the Government of Japan are aimed at establishing a sustainable human-centered society by combining new technologies such as sensor networks, edge computing, Internet of Things (IoT) ecosystems, artificial intelligence (AI), big data, and robotics. Many research works have been carried out with an increasing emphasis on the fundamentals of wireless sensor networks (WSN) for different applications; namely precision agriculture, environment, medical care, security, and surveillance.
Weeds are among the most harmful abiotic factors in agriculture, triggering significant yield loss worldwide. Remote sensing can detect and map the presence of weeds in various spectral, spatial, and temporal resolutions. This review aims to show the current and future trends of UAV applications in weed detection in the crop field.