Department of Agronomy, University of Zabol, Zabol, Sistan and Baluchestan, Iran.
PeerJ. 2023 Oct 2;11:e15417. doi: 10.7717/peerj.15417. eCollection 2023.
Undoubtedly, the importance of food and food security as one of the present and future challenges is not invisible to anyone. Nowadays, the development of methods for monitoring the nutrient content in crop products is an essential issue for implementing reasonable and logical soil properties management. The modeling technique can evaluate the soil properties of fields and study the subject of crop yield through soil management. This study aims to predict fruit yield and macro-nutrient content in plant organs of in response to soil elements using support vector regression (SVR).
In the spring of 2020, this study was done as a factorial test in a randomized complete block design with three replications. The first factor was the use of fertilizers in six levels: no fertilizer (control), cow manure (30 t ha), sheep manure (30 t ha), nanobiomic foliar application (2 l ha), silicone foliar application (3 l ha), and chemical fertilizer from urea, triple superphosphate, and potassium sulfate sources (200, 100, and 150 kg ha). In addition, four levels of vermicompost considering as the second factor: no vermicompost (control), 5, 10, and 15 t ha. Input data sets such as fruit yield and nitrogen, phosphorus, and potassium levels in the seeds, fruits, leaves, and roots are used to calibrate the probabilistic model of SP using SVR.
According to the results, when the data sets of the nitrogen, phosphorus, and potassium in the fruit uses as input, the accuracy of these models was higher than 80.0% (R = 0.807 for predicting fruit nitrogen; R = 0.999 for fruit phosphorus; R = 0.968 for fruit potassium). Also, the results of the prediction models in response to soil elements showed that the soil nitrogen content ranged from 0.05 to 1.1%, soil phosphorus from 10 to 59 mg kg, and soil potassium from 180 to 320 mg kg, which offers a suitable macro-nutrient content in the soil. Likewise, the best fruit nitrogen content ranged from 1.27 to 4.33%, fruit phosphorus from 15.74 to 26.19%, fruit potassium from 15.19 to 19.67%, and fruit yield from 2.16 to 5.95 kg per plant obtained under NPK chemical fertilizers and using 15 t ha of vermicompost.
Because the fruit values had the highest contribution in prediction than observed values, thus identified as the best plant organs in response to soil elements. Based on our findings, the importance of fruit phosphorus identifies as a determinant that strongly influenced melon prediction models. More significant values of soil elements do not affect increasing fruit yield and macro-nutrient content in plant organs, and excessive application may not be economical. Therefore, our studies provide an efficient approach with potentially high accuracy to estimate fruit yield and macro-nutrient in the fruits of in response to soil elements and cause a saving in the amount of fertilizer during the growing season.
毫无疑问，食品和食品安全作为当前和未来挑战之一的重要性对任何人来说都不是不可见的。如今，监测作物产品营养成分含量的方法的发展是实施合理和逻辑的土壤特性管理的重要问题。建模技术可以通过土壤管理来评估田间土壤特性和研究作物产量的主题。本研究旨在使用支持向量回归（SVR）预测果实产量和植物器官中的宏量营养素含量对土壤元素的响应。
2020 年春季，本研究采用三重复随机完全区组设计进行了一项析因试验。第一个因素是六种肥料的使用水平：不施肥（对照）、牛粪（30 t ha）、绵羊粪（30 t ha）、纳米生物叶面喷施（2 l ha）、硅叶面喷施（3 l ha）和来自尿素、过磷酸钙和硫酸钾的化学肥料（200、100 和 150 kg ha）。此外，第二个因素考虑了四种水平的蚯蚓粪：无蚯蚓粪（对照）、5、10 和 15 t ha。使用果实产量和种子、果实、叶片和根系中的氮、磷和钾水平等输入数据集，通过 SVR 对 SP 的概率模型进行校准。
结果表明，当将果实中的氮、磷和钾数据集用作输入时，这些模型的准确性高于 80.0%（预测果实氮的 R = 0.807；预测果实磷的 R = 0.999；预测果实钾的 R = 0.968）。此外，对土壤元素的预测模型的结果表明，土壤氮含量范围为 0.05 至 1.1%，土壤磷含量为 10 至 59 mg kg，土壤钾含量为 180 至 320 mg kg，这为土壤提供了适宜的大量营养素含量。同样，最佳果实氮含量范围为 1.27 至 4.33%，果实磷含量为 15.74 至 26.19%，果实钾含量为 15.19 至 19.67%，每株植物的果实产量为 2.16 至 5.95 kg，这些结果是在使用 NPK 化学肥料和 15 t ha 蚯蚓粪的情况下获得的。
由于果实值对预测的贡献大于观测值，因此被确定为对土壤元素反应的最佳植物器官。根据我们的发现，果实磷的重要性被确定为强烈影响甜瓜预测模型的决定因素。土壤元素的更高显著值不会影响增加果实产量和植物器官中的大量营养素含量，过量施用可能不经济。因此，我们的研究提供了一种有效的方法，具有潜在的高精度，可以估计对土壤元素的响应中的果实产量和果实中的大量营养素，并在生长季节节省肥料用量。
