Strawberry, the most significant berry crop, is characterized as a salt-sensitive plant. The present study aimed to examine ways to alleviate salinity symptoms (34 mM of NaCl in irrigation water) in strawberry plants. For this purpose, the osmolyte glycine betaine was foliarly applied, a mixture of bentonite–zeolite was added to the substrate, and a microbial product based on Bacillys amyloliquefaciens as a soil drench were tested in terms of plant growth and nutrient status, yield, fruit physiological and organoleptic characteristics, as well as phytochemical properties (phenolic compounds, carbohydrates, organic acids, anthocyanins, and antioxidant capacity), and soil physicochemical properties. Salinity severely reduced plant growth and yield, while the effects on fruit quality were also distinct. Treatments alleviated to some extent these negative effects. Plant nutrient content was not severely affected by product application, and neither were most of the soil physicochemical properties. Among the products applied, the mixture of bentonite plus zeolite and glycine betaine proved to be more efficient in ameliorating toxicity symptoms, as both treatments preserved plant hydric status and plant growth, while glycine betaine resulted in an almost 30% higher yield than the treatment with saline water.

In a field experiment, composted coffee waste (CW) was used as soil amendment at three different rates (2%, 4%, and 8%) in plots cultivated with wheat; the effects on the soil nematode community and plant growth were studied. By sampling twice, i.e., three and six months after the application of treatments (3MAA and 6MAA), the duration of these effects was also evaluated. Treatment using composted coffee waste (CW) led to an increased abundance of all soil nematodes, especially of bacterivores and fungivores, probably via a bottom-up effect of CW on the soil community. The most-affected genera were the enrichment opportunists Panagrolaimus and Rhabditis, which increased after CW addition in a dosage-dependent way at 3MAA, while at 6MAA they were replaced by general opportunists, mainly bacterivorous and fungivorous genera; a nematotoxic effect of CW was also observed in the 6MAA condition. The nematode indices and the metabolic footprint indicated an enriched and vigorous soil three months after CW addition and a lower enrichment status of the soil together with a higher fungal participation in the decomposition pathway six months after treatments. However, in the 6 MAA condition, the soil nutrient values were higher in the CW-treated plots. None of our treatments inhibited or enhanced plant growth.

In a greenhouse experiment, we studied the impact of Melia azedarach ripe fruit water extract (MWE), Furfural (a key ingredient of M. azedarach), and the commercial nematicide Oxamyl (Vydate^® 10 SL) on the soil free-living nematode community. Treatments were applied every 20 days for two months, and soil samples were collected 3 days after the last application (3DAA) and at the end of the cultivation period (34DAA). We assessed short- and long-term effects on nematode community structure, metabolic footprint, genus composition, and interaction networks. Oxamyl and Furfural significantly reduced bacterial and fungal feeder populations. MWE had no impact on free-living nematode populations. Oxamyl and Furfural-treated soil samples were dominated by Rhabditis at 3DAA and Meloidogyne spp. at 34DAA. On the contrary, MWE-treated soil showed a balanced distribution, with Rhabditis, Panagrolaimus, Mesorhabditis, and Diploscapter being equally abundant. MWE treatment exhibited higher diversity indices (Shannon and Simpson) and equitability. Network analysis showed that the Oxamyl network had the highest fragmentation, while the MWE and Furfural networks had higher cohesion compared to the control. Mesorhabditis spp. in the MWE network played a crucial role, being directly connected to the omnivore genera Thonus and Aporcelaimellus. Our results indicated that continuous MWE application, besides controlling Meloidogyne spp., could enhance the structure and stability of the soil-free-living nematode community.

This study introduces a novel methodology for predicting cotton yield by integrating machine learning (ML) with mechanistic soil modeling. This hybrid approach enhances yield prediction by combining data-driven ML techniques with soil process modeling. Using the developed yield model, yield curves for various nitrogen (N) levels can be constructed to identify the optimal N dose that maximizes yield. Estimating cotton N requirements is crucial, as growers often apply excessive N, exceeding the amount needed for maximum yield. By comparing the Mean Absolute Error (MAE) between predicted and observed cotton yield values across three ML algorithms, i.e., Random Forest (RF), XGBoost, and LightGBM, the RF model achieved the lowest error (422.6 kg/ha), outperforming XGBoost (446 kg/ha) and LightGBM (449 kg/ha). Additionally, the RF model exhibited high sensitivity to N fertilization, ranking N as the most influential variable in feature importance analysis. Furthermore, phosphorus (P) availability in the soil model was found to be a significant factor influencing the RF yield model, highlighting P’s crucial role in cotton growth and productivity.

Mineral nutrition management in sweet cherry orchards remains a critical challenge due to the lack of site-specific fertilization guidelines, particularly in Greece, a significant cherry-producing country. This study aimed to develop a predictive framework for total nutrient losses in sweet cherry orchards by proposing simplified estimations using fresh fruit yield as the sole input variable. Field experiments were conducted in two orchards with distinct rootstocks (MxM 14 and CAB-6P), analyzing soil properties, leaf nutrient status, and uptake patterns on different plant components. Results indicated that despite differences in soil texture and pH, nutrient availability was generally sufficient, with only Fe and Zn marginally below optimal levels in leaf tissue. Principal Component Analysis (PCA) revealed distinct nutrient distribution patterns, with N evenly distributed across fruits, peduncles, and prunings, while K was concentrated in fruits and peduncles, and Ca and Mg predominantly in fallen leaves. Notably, K was redistributed from leaves to fruits under high yields, evidenced by negative correlations between leaf biomass and K uptake. Strong relationships (r² > 0.8) were found between fresh fruit yield and uptake of N, P, K, Mg, B, and Cu, enabling reliable predictions of total nutrient losses. Estimated annual nutrient removals were 85.6 kg ha⁻¹ N, 8.94 kg ha⁻¹ P, 42.7 kg ha⁻¹ K, and 12.0 kg ha⁻¹ Mg, with significant fractions retained in prunings and fallen leaves (e.g., 51.8 kg ha⁻¹ N, 6.2 kg ha⁻¹ P). The developed yield-based models provide a practical tool for optimizing fertilization strategies, while our findings highlight the potential for nutrient recycling through sustainable residue management.