Digital agriculture involving different tools and management practices has advanced considerably in recent years, intending to overcome climate risk and reduce food insecurity. Climate change and its impacts on agricultural production and food security are significant sources of public concern worldwide. The objective of this study was to provide an overview of the potential impact of digital agriculture technologies and practices that can reduce greenhouse gas emissions and enhance productivity while ensuring food security. Based on a comprehensive survey of the previously published works, it was found that due to global warming, altered precipitation patterns, and an increase in the frequency of extreme events, climate change has negatively impacted food security by reducing agricultural yields, slowing animal growth rates, and decreasing livestock productivity. The reviewed works also suggest that using digital technology in agriculture is necessary to mitigate the effect of climate change and food insecurity. In addition, issues regarding creating sustainable agricultural food systems, minimizing environmental pollution, increasing yields, providing fair and equitable food distribution, and reducing malnutrition leading to food security were discussed in detail. It was shown that while digital agriculture has a crucial role in mitigating climate change and ensuring food security, it requires a concerted effort from policymakers, researchers, and farmers to ensure that the benefits of digitalization are realized in a sustainable and equitable manner.

The region of Al-Jabal Al-Akhdar in northeastern Libya has undergone rapid, wide-ranging changes in the land use and land cover (LULC) intensified by the conversion of natural resources for food purpose, urbanization, and other socioeconomic benefits. This study examined the use of geographic information system (GIS) and remote sensing techniques to gain a quantitative understanding of the spatiotemporal dynamics of LULC. In addition, the major factors behind LULC changes and decline of natural vegetation in the region were analyzed. A post-classification comparison approach was used to detect LULC changes in the study area between 1985 and 2017 using four Landsat images from 1985, 2000, 2010, and 2017. The observed changes were indicative of a decrease in the expanse of the natural Mediterranean forest which lost 9018 ha over 32 years, 39% of its total area, with the highest deforestation rate registered between 2010 and 2017 estimated at 513 ha. year⁻¹. Orchards and rain-fed agriculture lands gained 4095 ha, which matches 55% of initial area, whereas the land under irrigated crops increased by 2266 ha, about 85% of the original area. The area of urban and built-up land in 2017 was more than double in 1985 and achieved the highest urbanization rate between 2010 and 2017 at 203 ha.year⁻¹. Results indicate an unstable trend of bare and low vegetation lands which generally increased by about 50%. From the outcomes of this research, it is strongly recommended that urgent measures be taken to conserve the natural forest and to achieve a rational use of agricultural land in the region of Al-Jabal Al-Akhdar.

Excessive use of antibiotics in the healthcare sector and livestock farming has amplified antimicrobial resistance (AMR) as a major environmental threat in recent years. Abiotic stresses, including soil salinity and water pollutants, can affect AMR in soils, which in turn reduces the yield and quality of agricultural products. The objective of this study was to investigate the effects of antibiotic resistance and abiotic stresses on antimicrobial resistance in agricultural soils. A systematic review of the peer-reviewed published literature showed that soil contaminants derived from organic and chemical fertilizers, heavy metals, hydrocarbons, and untreated sewage sludge can significantly develop AMR through increasing the abundance of antibiotic resistance genes (ARGs) and antibiotic-resistant bacteria (ARBs) in agricultural soils. Among effective technologies developed to minimize AMR’s negative effects, salinity and heat were found to be more influential in lowering ARGs and subsequently AMR. Several strategies to mitigate AMR in agricultural soils and future directions for research on AMR have been discussed, including integrated control of antibiotic usage and primary sources of ARGs. Knowledge of the factors affecting AMR has the potential to develop effective policies and technologies to minimize its adverse impacts.

Optimum microclimate parameters, including air temperature (T), relative humidity (RH) and vapor pressure deficit (VPD) that are uniformly distributed inside greenhouse crop production systems are essential to prevent yield loss and fruit quality. The objective of this research was to determine the spatial and temporal variations in the microclimate data of a commercial greenhouse with tomato plants located in the mid-west of Iran. For this purpose, wireless sensor data fusion was incorporated with a membership function model called Optimality Degree (OptDeg) for real-time monitoring and dynamic assessment of T, RH and VPD in different light conditions and growth stages of tomato. This approach allows growers to have a simultaneous projection of raw data into a normalized index between 0 and 1. Custom-built hardware and software based on the concept of the Internet-of-Things, including Low-Power Wide-Area Network (LoRaWAN) transmitter nodes, a multi-channel LoRaWAN gateway and a web-based data monitoring dashboard were used for data collection, data processing and monitoring. The experimental approach consisted of the collection of meteorological data from the external environment by means of a weather station and via a grid of 20 wireless sensor nodes distributed in two horizontal planes at two different heights inside the greenhouse. Offline data processing for sensors calibration and model validation was carried in multiple MATLAB Simulink blocks. Preliminary results revealed a significant deviation of the microclimate parameters from optimal growth conditions for tomato cultivation due to the inaccurate timer-based heating and cooling control systems used in the greenhouse. The mean OptDeg of T, RH and VPD were 0.67, 0.94, 0.94 in January, 0.45, 0.36, 0.42 in June and 0.44, 0.0, 0.12 in July, respectively. An in-depth analysis of data revealed that averaged OptDeg values, as well as their spatial variations in the horizontal profile were closer to the plants’ comfort zone in the cold season as compared with those in the warm season. This was attributed to the use of heating systems in the cold season and the lack of automated cooling devices in the warm season. This study confirmed the applicability of using IoT sensors for real-time model-based assessment of greenhouse microclimate on a commercial scale. The presented IoT sensor node and the Simulink model provide growers with a better insight into interpreting crop growth environment. The outcome of this research contributes to the improvement of closed-field cultivation of tomato by providing an integrated decision-making framework that explores microclimate variation at different growth stages in the production season.

Phytophthora meadii (McRae) is a hemibiotrophic oomycete fungus that infects tender nuts, growing buds, and crown regions, resulting in fruit, bud, and crown rot diseases in arecanut (Areca catechu L.), respectively. Among them, fruit rot disease (FRD) causes serious economic losses that are borne by the growers, making it the greatest yield-limiting factor in arecanut crops. FRD has been known to occur in traditional growing areas since 1910, particularly in Malnad and coastal tracts of Karnataka. Systemic surveys were conducted on the disease several decades ago. The design of appropriate management approaches to curtail the impacts of the disease requires information on the spatial distribution of the risks posed by the disease. In this study, we used exploratory survey data to determine areas that are most at risk. Point pattern (spatial autocorrelation and Ripley’s K function) analyses confirmed the existence of moderate clustering across sampling points and optimized hotspots of FRD were determined. Geospatial techniques such as inverse distance weighting (IDW), ordinary kriging (OK), and indicator kriging (IK) were performed to predict the percent severity rates at unsampled sites. IDW and OK generated identical maps, whereby the FRD severity rates were higher in areas adjacent to the Western Ghats and the seashore. Additionally, IK was used to identify both disease-prone and disease-free areas in Karnataka. After fitting the semivariograms with different models, the exponential model showed the best fit with the semivariogram. Using this model information, OK and IK maps were generated. The identified FRD risk areas in our study, which showed higher disease probability rates (>20%) exceeding the threshold level, need to be monitored with the utmost care to contain and reduce the further spread of the disease in Karnataka.