Monoculture involves growing the same crop on the same land over at least two crop cycles. Continuous monoculture can increase the population density of pests and pathogens over time, thereby reducing agricultural yields and increasing dependence on chemical inputs. Crop rotation is an agricultural practice that involves systematically and sequentially planting different crops in the same field over multiple growing seasons. This review explores the advantages of crop rotation and its contribution to promoting sustainable farming practices, such as legume integration and cover cropping. It is based on a thematic literature review of peer-reviewed studies published between 1984 and 2025. We found that crop rotation can significantly improve soil structure and organic matter content and enhance nutrient cycling. Furthermore, soil organic carbon increased by up to 18% when legumes were included in rotations compared to monoculture systems in Europe, while also mitigating greenhouse gas emissions, enhancing carbon sequestration, and decreasing nutrient leaching and pesticide runoff. Farmers can adopt several strategies to optimise crop rotation benefits, such as diversification of various crops, legume integration, cultivation of cover crops, and rotational grazing. These practices ensure agricultural sustainability and food security and support climate resilience.

Agriculture faces several challenges to use the available resources in a more environmentally sustainable manner. One of the most significant is to develop sustainable water management. The modern Internet of Things (IoT) techniques with real-time data collection and visualisation can play an important role in monitoring the readily available moisture in the soil. An automated Arduino-based low-cost capacitive soil moisture sensor has been calibrated and developed for data acquisition. A sensor- and soil-specific calibration was performed for the soil moisture sensors (SKU:SEN0193 - DFROBOT, Shanghai, China). A Repeatability and Reproducibility study was conducted by range of mean methods on clay loam, sandy loam and silt loam soil textures. The calibration process was based on the data provided by the capacitive sensors and the continuously and parallelly measured soil moisture content by the thermogravimetric method. It can be stated that the response of the sensors to changes in soil moisture differs from each other, which was also greatly influenced by different soil textures. Therefore, the calibration according to soil texture was required to ensure adequate measurement accuracy. After the calibration, it was found that a polynomial calibration function (R² ≥ 0.89) was the most appropriate way for modelling the behaviour of the sensors at different soil textures.

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.

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.

Agricultural soil has great potential to address climate change issues, particularly the rise in atmospheric CO₂ levels. It offers effective remedies, such as increasing soil carbon content while lowering atmospheric carbon levels. The growing interest in inoculating soil with live microorganisms aims to enhance agricultural land carbon storage and sequestration capacity, modify degraded soil ecosystems, and sustain yields with fewer synthetic inputs. Agriculture has the potential to use soil microalgae as inoculants. However, the significance of these microorganisms in soil carbon sequestration and soil carbon stabilization under field conditions has yet to be fully understood. Large-scale commercial agriculture has focused on the development and use of inoculation products that promote plant growth, with a particular emphasis on enhancing yield attributes. Gaining more profound insights into soil microalgae’s role in soil carbon cycling is necessary to develop products that effectively support soil carbon sequestration and retention. This review comprehensively explores the direct and indirect mechanisms through which soil microalgae contribute to soil carbon sequestration, highlighting their potential as microbial inoculants in agricultural settings. This study underlines the need for more research to be conducted on microalgae inoculation into agricultural soil systems aimed at mitigating carbon emissions in the near future.

Phacelia tanacetifolia, an excellent cover, green manure and honey crop is now widely cultivated throughout the world. One of its principal European seed production regions is north-western Hungary, where the recent withdrawal of a potent herbicide, linuron, created a new challenge for many growers. The goal of this study is to identify the main factors determining weed species composition in the phacelia fields of the region and to assess the efficiency of tine harrow and clopyralid herbicide in reducing weed abundance and biomass. We carried out a series of weed surveys across the study region following a two-level design: (i) we estimated the cover of all weed species in 205 fields (broad-scale survey, BS); and (ii) in 22 of these fields, we provided more precise biomass measurements (counting the individuals and measuring the dry weights of all weed species) in microplots samples (fine-scale survey; FS). To characterize the fields, 34 background variables were also collected for all of the studied fields. In both investigations, Chenopodium album was by far the most abundant weed. Within the BS, using a minimal adequate model containing 11 terms with significant net effects, 20.93% of the total variation in weed species data could be explained. The variation in species composition was determined by environmental factors (soil pH, clay and K; precipitation and temperature), non-chemical management variables (crop cover, preceding crop, irrigation and tillage system) and herbicides (linuron and clopyralid). Variation partitioning demonstrated the dominance of environmental and cultural components in shaping the weed species composition. Although the effect of mechanical treatments was most likely masked in the BS by the soil properties, our FS suggests that tine harrow could efficiently decrease the total number and biomass of weeds and can be a useful tool in the phacelia management of the future.