Greenhouse gas and ammonia emissions from soil: the effect of organic matter and fertilisation method

The implementation of environmentally friendly agricultural policies has increased the need to compare agricultural aspects of conventional (CON) and organic farming (ORG) systems. The objective of the present work was to compare the effects of an organic and conventional long‐term experiment on bacterial and fungal biomass and activity, as well as soil CO ₂ emission and readily available nitrogen forms in a soil cultivated with Helianthus annuus L. The microbial biomass was more active and abundant in ORG as well as soil CO ₂ emission. Despite being less abundant, fungi were more active than bacteria in both ORG and CON experiments. 16S rRNA gene sequencing showed that the ORG treatment had a significantly greater bacterial richness than CON. Cyanobacteria, Actinobacteria and Proteobacteria were the most abundant phyla contributing more than others to the differences between the two systems. Moreover, the soil and content was not significantly different between ORG and CON, while was less in ORG. ORG sunflower yield was significantly less compared with CON. While much remains to be discovered about the effects of these agricultural practices on soil chemical properties and microbial diversity, our findings may contribute to this type of investigation.

Implementation of the Internet of Things (IoT) is revolutionizing agricultural techniques in modern precision irrigation systems. This research was conducted in an open field trial on processing tomato to evaluate the application of machine learning (ML)-based decision support systems (DSS) for predictive optimization of deficit irrigation strategies. A smart irrigation system (SIS) was adopted to investigate the effect of different management strategies under water deficit conditions. Four irrigation treatments using drip irrigation were tested: Full irrigation (FI) - (100% of ETc), Deficit irrigation (DI₁) - (80% of ETc), Regulated Deficit irrigation (RDI) - (60-80-60% of ETc) and Deficit irrigation (DI₂) - (60% of ETc). The trial was monitored via an ML-based SIS connected to a network of soil moisture and temperature sensors. This study aims to: (i) assess how processing tomato responds to controlled deficit irrigation using an ML-based DSS; (ii) examine the correlation between various water productivity indices (WP, TYWP, MYWP, and IWP) and tomato yields and quality; (iii) investigate the impact of temperature and thermal stress on tomato performances under different irrigation regimes. Results show significant effects of deficit irrigation on growth and yield parameters (p ≤ 0.001). Both RDI and DI₁ achieved high water productivity, with no significant differences compared to FI. In the 2022 growing season, deficit irrigation, particularly DI₁, resulted in higher WP values, saving 32.66% of water and improving fruit quality. The study emphasizes the potential of deficit irrigation for achieving high yields and quality in processing tomatoes. However, the ML-based DSS, while effective in water management, requires enhanced sensitivity to crop susceptibility to heat and water stresses.

Proximal optical sensors (POSs) are effective devices for monitoring the development of crops and the nitrogen (N) status of plants. POSs are both useful and necessary in facilitating the reduction of N losses into the environment and in attaining higher nitrogen use efficiency (NUE). To date, no comparison of these instruments has been made on quinoa. A field experiment conducted in Tuscany, Italy, with different POSs, has assessed the development of quinoa with respect to N status. Three sets of POSs were used (SPAD-502, GreenSeeker, and Canopeo App.) to monitor quinoa development and growth under different types of fertilizers (digestate and urea) and levels of N fertilization (100, 50, and 0 kg N ha⁻¹). The present findings showed that in-season predictions of crop biomass at harvest by SPAD-502 and GreenSeeker optical sensors were successful in terms of the coefficient of determination (R² = 0.68 and 0.82, respectively) and statistical significance (p < 0.05), while the Canopeo App. was suitable for monitoring the plant´s canopy expansion and senescence. The relative error (RE%) showed a remarkably high performance between observed and predicted values, 5.80% and 4.12% for GreenSeeker and SPAD-502, respectively. Overall, the POSs were effective devices for monitoring quinoa development during the growing season and for predicting dry biomass at harvest. However, abiotic stresses (e.g., heat-stress conditions at flowering) were shown to reduce POSs’ accuracy when estimating seed yields at harvest, and this problem will likely be overcome by advancing the sowing date.