Fertilizer-use efficiency is one of the most critical concerns in rice cultivation to reduce N losses, increase yields, and improve crop management. The effects of a new polymeric-coated controlled-release fertilizer (CRF) were compared to those of other slow-release and traditional fertilizers in a microscale experiment, which was carried out in cuvettes under partly controlled ambient conditions, and a large-scale field experiment. To evaluate the fertilizer’s efficiency, nitrogen and water-use efficiency were calculated using the measurement of different photosynthetic and crop yield parameters. Improved responses regarding some of the analyzed physiological and growth parameters were observed for those plants fertilized with the new CRF. In the microscale experiment, significantly increased yields (ca. 35%) were produced in the plants treated with CRF as compared to traditional fertilizer. These results were in accordance with ca. 24% significant increased levels of N in leaves of CRF-treated plants, besides increased P, Fe, Mn, and cytokinin contents. At the field scale, similar yields were obtained with the slow-release or traditional fertilizers and CRF at a 20% reduced N dose. The new controlled-release fertilizer is a urea-based fertilizer coated with lignosulfonates, which is cheaply produced from the waste of pulp and wood industries, containing humic acids as biostimulants. In conclusion, CRF is recommended to facilitate rice crop management and to reduce contamination, as it can be formulated with lower N doses and because it is ecological manufacturing.

Rice is considered one of the most important crops in the world. According to the Food and Agriculture Organization of the United Nations (FAO), rice production has increased significantly (156%) during the last 50 years, with a limited increase in cultivated area (24%). With the recent advances in remote sensing technologies, it is now possible to monitor rice crop production for a better understanding of its management at field scale to ultimately improve rice yields. In this work, we monitor within-field rice production of the two main rice varieties grown in Valencia (Spain) JSendra and Bomba during the 2020 season. The sowing date of both varieties was May 22–25, while the harvesting date was September 15–17 for Bomba and October 5–8 for JSendra. Rice yield data was collected over 66.03 ha (52 fields) by harvesting machines equipped with onboard sensors that determine the dry grain yield within irregular polygons of 3–7 m width. This dataset was split in two, selecting 70% of fields for training and 30% for validation purposes. Sentinel-2 surface reflectance spectral data acquired from May until September 2020 was considered over the test area at the two different spatial resolutions of 10 and 20 m. These two datasets were combined assessing the best combination of spectral reflectance bands (SR) or vegetation indices (VIs) as well as the best timing to infer final within-field yields. The results show that SR improves the performance of models with VIs. Furthermore, the correlation of each spectral band and VIs with the final yield changes with the dates and varieties. Considering the training data, the best correlation with the yields is obtained on July 4, with R² for JSendra of 0.72 at 10 m and 0.76 at 20 m resolution, while the R² for Bomba is 0.87 at 10 m and 0.92 at 20 m resolution. Based on the validation dataset, the proposed models provide within-field yield modelling Mean Absolute Error (MAE) of 0.254 t×ha⁻¹ (Mean Absolute Percentage Error, MAPE, of 3.73%) for JSendra at 10 m (0.240 t×ha⁻¹; 3.48% at 20 m) and 0.218 t×ha⁻¹ (MAPE 5.82%) for Bomba (0.223 t×ha⁻¹; 5.78% at 20 m) on July 4, that is three months before harvest. At parcel level the model’s MAE is 0.176 t×ha⁻¹ (MAPE 2.61%) for JSendra and 0.142 t×ha⁻¹ (MAPE 4.51%) for Bomba. These results confirm the close correlation between the rice yield and the spectral information from satellite imagery. Additionally, these models provide a timeliness overview of underperforming areas within the field three months before the harvest where farmers can improve their management practices. Furthermore, it highlights the importance of optimum agronomic management of rice plants during the first weeks of rice cultivation (40–50 days after sowing) to achieve high yields.

World agriculture is facing a great challenge since it is necessary to find a sustainable way to increase food production. Current trends in advancing the agriculture sector are based on leveraging remote sensing technology and the use of biostimulants. However, the efficient implementation of both of these on a commercial scale for the purposes of productivity improvement remains a challenge. Thus, by proposing a crop monitoring strategy based on remote sensing data, this paper aims to verify and anticipate the impact of applying a Glycinebetaine biostimulant (GB) on the final yield. The study was carried out in a rice-producing area in Eastern Spain (Valencia) in 2021. GB was applied by drone 33 days after sowing (tillering phase). Phenology was monitored and crop production parameters were determined. Regarding satellite data, Sentinel-2 cloud-free images were obtained from sowing to harvest, using the bands at 10 m. Planet data were used to evaluate the results from Sentinel-2. The results show that GB applied 33 days after sowing improves both crop productive parameters and commercial yield (13.06% increase). The design of the proposed monitoring strategy was based on the dynamics and correlations between the visible (green and red) and NIR bands. The analysis showed differences when comparing the GB and control areas, and permitted the determination of the moment in which the effect of GB on yield (tillering and maturity) may be greater. In addition, an index was constructed to verify the crop monitoring strategy, its mathematical expression being: NCMI = (NIR − (red + green))/(NIR + red + green). Compared with the other VIs (NDVI, GNDVI and EVI2), the NCMI presents a greater sensitivity to changes in the green, red and NIR bands, a lower saturation phenomenon than NDVI and a better monitoring of rice phenology and management than GNDVI and EVI2. These results were evaluated with Planet images, obtaining similar results. In conclusion, in this study, we confirm the improvement in rice crop productivity by improving sustainable plant nutrition with the use of biostimulants and by increasing the components that define crop yield (productive tillers, spikelets and grains). Additionally, crop monitoring using remote sensing technology permits the anticipation and understanding of the productive behavior and the evolution of the phenological stages of the crop, in accordance with crop management.

A greenhouse experiment was conducted to study the effects of the O₂ root zone level and grafting on chlorophyll fluorescence, photosynthesis and growth of cherry tomato grown in a hydroponic system. Two O₂ concentrations in the root zone, namely Ox (saturation level) and Ox- (2–3 mg L⁻¹), were applied for 30 days on self-grafted cherry tomato Dreamer or grafted onto the hybrids Arnold, Beaufort, Maxifort and Top Pittam. Root hypoxia increased minimum fluorescence (by 10%) while it decreased variable fluorescence and the maximum quantum yield of PSII (up to 16 and 8%, respectively). Moreover, it reduced leaf photosynthesis, transpiration and stomatal conductance (by 12, 17 and 13%, respectively), whereas it increased leaf electrolyte leakage (by 2.1%). The graft combinations showed a different ability in buffering the effects of root hypoxia on plant growth and related components, and these differences were related to their root biomass. The minimum fluorescence was negatively correlated to plant growth, so it may be a useful indicator to select tolerant rootstocks to root hypoxia. Our results suggest the occurrence of both diffusive and metabolic constraints to tomato photosynthesis under root hypoxia, a condition that can be mitigated by selecting rootstocks with a more developed root system.

This research aims to evaluate the effect of the potassium–nitrogen balance on strawberry productivity and quality parameters in soilless conditions. A trial was conducted at the Agronomic and Veterinary Hassan II Institute glasshouse in Rabat, during the 2018–2019 crop year. The pot experiment began on November 13 using three different strawberry cultivars: Fortuna, San Andreas and Sabrina. Three nutrient solutions were tested by increasing the potassium–nitrogen balance during the growth stage and decreasing it during the fruit-production stage: S1 (1.3/2.0), S2 (2.6/1.0) and S3 (3.0/0.6). For all treatments, the total dose of fertilizing elements N, P, K, Ca and Mg was identical. The experimental design was a randomized complete block with three replications. The results show that strawberry plants receiving a nutrient solution with a high K:N balance during the growth period and a low balance during the production period present the higher growth and fruit levels. Moreover, the nutrient solution with the 2.6/1.0 balance significantly increased the chlorophyll index by 8%, yield by 30% (7.9 t ha⁻¹), total soluble solids and dry matter content by 14% and 15%, respectively, and improved taste and fruit shelf-life by 10% and 19%, respectively.