Low-cost air temperature sensors are an emerging theme in environmental monitoring. These sensors offer the advantage of making microclimate monitoring feasible due to their affordability. However, they are limited by the quality of the data they provide; in many cases, they have been reported to have presented errors in the sensor readings. These errors have been shown to improve after calibration was applied. The lack of a comprehensive understanding of the available calibration techniques, models, and sensor types has led to studies presenting heterogeneity in models and techniques alongside different performance metrics. To address this gap, this study conducted a systematic review following the PRISMA guidelines, reviewing studies from 2015 to 2024 across the databases Web of Science and Scopus, alongside the search engine Google Scholar. The aim was to identify the calibration techniques and models, the commercially available low-cost air temperature sensors used, the performance metrics utilised, and the calibration settings. The findings presented three main categories of calibration models utilised in the collected studies: linear, polynomial, and machine learning. Twenty-two commercially available low-cost sensors were identified, with the DHT22 sensor being the most utilised. Indoor settings were identified as the most preferred for conducting calibrations. Key challenges included limitations in reported results for calibration by the studies, the use of different performance metrics across studies, insufficient studies conducting calibration, and the diversity in sensor types utilised.

Although herbicides are essential for global agriculture and controlling weeds, they impact soil microbial communities and CO2 emissions. However, the effects of herbicides, tillage systems, and nitrogen fertilisation on CO2 emissions under different environmental conditions are poorly understood. This review explores how various agricultural practices and inputs affect CO2 emissions and addresses the impact of pest-management strategies, tillage systems, and nitrogen fertiliser usage on CO2 emissions using multiple databases. Key findings indicate that both increased and decreased tendencies in greenhouse gas (GHG) emissions were observed, depending on the herbicide type, dose, soil properties, and application methods. Several studies reported a positive correlation between CO2 emissions and increased agricultural production. Combining herbicides with other methods effectively controls emissions with minimal chemical inputs. Conservation practices like no-tillage were more effective than conventional tillage in mitigating carbon emissions. Integrated pest management, conservation tillage, and nitrogen fertiliser rate optimisation were shown to reduce herbicide use and soil greenhouse gas emissions. Fertilisers are similarly important; depending on the dosage, they may support yield or harm the soil. Fertiliser benefits are contingent on appropriate management practices for specific soil and field conditions. This review highlights the significance of adaptable management strategies that consider local environmental conditions and can guide future studies and inform policies to promote sustainable agriculture practices worldwide.

Site-specific management requires the identification of treatment areas based on homogeneous characteristics. This study aimed to determine whether soil mapping based on apparent soil electrical conductivity (ECa) is suitable for mapping soil properties of fields with topographic heterogeneity. Research was conducted on two neighbouring fields in Fejér county, Hungary, with contrasting topographic heterogeneity. To characterise the spatial variability of soil attributes, ECa was measured and supplemented by obtaining soil samples and performing soil profile analysis. The relationship between ECa and soil physical and chemical properties was analysed using correlation, principal component, and regression analyses. The research revealed that the quality and strength of the relationship between ECa and soil remarkably differed in the two studied fields. In homogeneous topographic conditions, ECa was weakly correlated with elevation as determined by soil physical texture and nutrient content in a strong (R² = 0.72) linear model. On the other hand, ECa was significantly determined by elevation in heterogeneous topographic conditions in a moderate (R² = 0.47) linear model. Consequently, ECa-based soil mapping can only be used to characterise the soil, thus delineating management zones under homogeneous topographic conditions.

This work aims to compare and statistically analyze Normalized Difference Vegetation Index (NDVI) values provided by GreenSeeker handheld crop sensor measurements and calculate NDVI values derived from the MicaSense RedEdge-MX Dual Camera, to predict in-season winter wheat (Triticum aestivum L.) yield, improving a yield prediction model with cumulative growing degree days (CGDD) and days from sowing (DFS) data. The study area was located in Mosonmagyaróvár, Hungary. A small-scale field trial in winter wheat was constructed as a randomized block design including Environmental: N-135.3, P2O5-77.5, K2O-0; Balance: N-135.1, P2O5-91, K2O-0; Genezis: N-135, P2O5-75, K2O-45; and Control: N, P, K 0 kg/ha. The crop growth was monitored every second week between April and June 2022 and 2023, respectively. NDVI measurements recorded by GreenSeeker were taken at three pre-defined GPS points for each plot; NDVI values based on the MicaSense camera Red and NIR bands were calculated for the same points. Results showed a significant difference (p ≤ 0.05) between the Control and treated areas by GreenSeeker measurements and Micasense-based calculated NDVI values throughout the growing season, except for the heading stage. At the heading stage, significant differences could be measured by GreenSeeker. However, remotely sensed images did not show significant differences between the treated and Control parcels. Nevertheless, both sensors were found suitable for yield prediction, and 226 DAS was the most appropriate date for predicting winter wheat’s yield in treated plots based on NDVI values and meteorological data.