Abstract. Although closed soilless culture is useful for saving water and fertilizers with minimizing environmental pollution, adequate management of nutrient solutions is still not stabilized in greenhouse cultivation. In order to investigate the problems occurred in closed soilless culture of Paprika (Capsicum annuum L., cv. Fiesta), we compared ion balance, fruit yield, and the water and fertilizer use efficiencies in the closed system with those in the open system. The plants were grown in rockwool culture with a nutrient solution of EC 2.5 dS・m in the open system was 20% higher, but the total water use per fruit was not significantly different between the two systems, while t total fertilizer use per fruit was 78% higher in the closed system. Amount of marketable fruits was not significantly different between the two systems. We concluded that the increase in K + supply and the replenishment of recycled nutrient solution every four weeks were required for preventing the imbalance or depletion of nutrients in the close soilless culture of paprika plants to get more balanced nutrient composition during whole cultivation period.Additional key words: ratio of nutrient composition, recycled soilless culture, reused nutrient solution
The urban hydroponic production system is accelerating industrialization in step with the potentials for reducing environmental impact. In contrast, establishing sustainable fertilizer dosing techniques still lags behind the pace of expansion of the system. The reproducibility of root-zone nutrient dynamics in the system is poorly understood, and managing nutrients has so far primarily relied on periodic discharge or dumping of highly concentrated nutrient solutions. Here, we assayed root-zone nutrient concentration changes using three possible nutrient dosing types. Three Brassica species were hydroponically cultivated in a controlled environment to apply the nutrient absorption and transpiration parameters to the simulation analysis. We found that nutrient dosing based on total ion concentration could provide more reproducible root-zone nutrient dynamics. Our findings highlight the nutrient absorption parameter domain in management practice. This simplifies conventional nutrient management into an optimization problem. Collectively, our framework can be extended to fertilizer-emission-free urban hydroponic production.
Crop fresh weight and leaf area are considered non-destructive growth factors due to their direct relation to vegetative growth and carbon assimilation. Several methods to measure these parameters have been introduced; however, measuring these parameters using the existing methods can be difficult. Therefore, a non-destructive measurement method with high versatility is essential. The objective of this study was to establish a non-destructive monitoring system for estimating the fresh weight and leaf area of trellised crops. The data were collected from a greenhouse with sweet peppers (Capsicum annuum var. annuum); the target growth factors were the crop fresh weight and leaf area. The crop fresh weight was estimated based on the total system weight and volumetric water content using a simple formula. The leaf area was estimated using top-view images of the crops and a convolutional neural network (ConvNet). The estimated crop fresh weight and leaf area exhibited average R2 values of 0.70 and 0.95, respectively. The simple calculation was able to avoid overfitting with fewer limitations compared with the previous study. ConvNet was able to analyze raw images and evaluate the leaf area without additional sensors and features. As the simple calculation and ConvNet could adequately estimate the target growth factors, the monitoring system can be used for data collection in practice owing to its versatility. Therefore, the proposed monitoring system can be widely applied for diverse data analyses.
Nitrate management in agricultural systems has mainly been established based on nitrate supply and the yield response curve. In the case of intensive fertilization systems such as soilless culture, the nitrate amount usually remains above the curve's optimal point. A surplus nutrient supply under these conditions could result in the excessive emission of chemical fertilizers. However, very few studies have developed a decision-making process for the efficient use of nitrate under the soilless culture system online. This study was conducted to develop an indicator related to the absorption of nitrate that can be applied in online systems utilizing the monitored irrigation and drainage amount data, electrical conductivity (EC), and the nitrate analysis data of irrigation and drainage. In the simulation, a stochastic change was generated for the nutrient absorption rate. The cultivation experiment verified the theoretical prediction, and a higher correlation of tomato yield with the nitrate absorption indicator was confirmed than with the nitrate supply amount. Also, the normalization of indicator and tomato yield showed dynamic time-series responses. The simulation and cultivation experiments showed that the indicator related to nitrate absorption estimated by online EC, irrigation, and drainage monitoring provides useful theoretical and experimental frameworks regarding efficient resource management decisions.
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Standardized cultivation systems are crucial for establishing reproducible agronomic techniques. Especially stone wool-based cultivation is governed by standardized specifications and provides a controllable root-zone environment. However, the effects of stone wool cover incision on root-zone variability have rarely been studied. Therefore, in this study, we focused on the effect of the stone wool cover incision method on environmental variations and their subsequent effects on tomato plant productivity. Stone wool slab plastic covers represent a core component of this substrate system that can potentially affect the performance of water control techniques. We designed a cover incision method to create four different levels of drainage performances that were tested by cultivating tomato plants (Solanum lycopersicum “Dafnis”). The water content, root-zone temperature, and dissolved oxygen were measured and analyzed relative to the tomato yield. We found that the incision level with the lowest drainage performance showed a lower air-root zone temperature correlation slope than those of slabs with favorable drainage conditions. Furthermore, these slabs had low dissolved oxygen levels (3.2 mg/L); nevertheless, the tomatoes grown in the slabs with incision level showing the lowest drainage performance had greater fruit yield (6,748 g/plant) than those in the slabs with favorable drainage conditions (6,160 g/plant). Furthermore, the normalized yield separation timing between treatments coincided with the hotter air temperature (27°C average) periods. We noted that manipulating the cover incision process consequently entailed variations in the correlation slope between the air temperature and root-zone temperature in the substrate. Our results reveal another trade-off relationship in the conventional perspective on the drainage performance effects and provide insights into further optimization of crop production and water use in the stone wool-based system.
In closed-loop soilless culture systems (SCS), ion concentration and ionic balance are important factors to be considered for stable management of nutrient solutions. For maintaining appropriate ion concentration and ion balance, various techniques of nutrient analysis and prediction are required. Through nutrient management modelling, nutrient variations in the closed-loop soilless culture systems using nutrient replenishment methods can be better understood and predicted. Deep learning algorithms could be a methodology to predict ion concentrations using environments and growth data. A trained deep learning model has been found to accurately estimate ion concentration and balance in closed-loop SCS. Applications of theoretical modelling and artificial intelligence can thus be useful for the nutrient management of closed-loop SCS in greenhouses and vertical farms.
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