Examinando por Materia "soil fertility"

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Impact of Green Manuring and Nitrogen Fertilization on Rice Cultivation: A Peruvian Amazon Forest Study in San Martín Province
(Scielo Preprint, 2024-03-12) Arevalo Aranda, Yuri Gandhi; Rodriguez Toribio, Elmer; Rosillo, Leodan; Diaz Chuqizuta, Henry; Torres Chávez, Edson Esmith; Cruz Luis, Juancarlos Alejandro; Siqueira Bahia, Rita de Cassia; Perez Porras, Wendy Elizabeth
Green manuring is an environmentally friendly technology aimed at providing nutrients to plants, enhancing soil fertility, mitigating soil degradation, controlling weeds and pests, and decreasing reliance on inorganic fertilizers. However, it requires dissemination and support to be adopted, especially in the poorest agricultural communities in Latin America. The study was conducted at the El Porvenir INIA in San Martín, Perú; it assessed two treatment sets: (1) green manure Crotalaria juncea (CroJ), Canavalia ensiforme (CanE), no green manure; and (2) nitrogen fertilizer dose (FN75, FN100). It was arranged in a split-plot design with four replications. During the experiment we detected an important fluctuation in soil parameters, however, it is the diminished levels of soil carbon and nitrogen, which were presumably the outcomes of microorganism processes. Otherwise, we observed that CanE significantly reduced the diseased tillers by "White Leaf Virus" (RHBV) by 2.82% compared to the control. The superior outcomes were achieved through CanE, and the highest yield was 8.36 t.ha¯¹ with the CanE - FN100 treatment. Additionally, the nutritional quality of rice was not altered by green manures or chemical nitrogen fertilization doses tested.
Vis-NIR spectroscopy and machine learning for prediction of soil fertility indicators and fertilizer recommendation in Andean highland and rainforest agroecosystems
(MDPI, 2026-04-26) Pizarro Carcausto, Samuel Edwin; Ccopi Trucios, Dennis; Ortega Quispe, Kevin Abner; Contreras Pino, Duglas Lenin; Ñaupari, Javier; Cano, Deyvis; Patricio Rosales , Solanch Rosy; Loayza, Hildo; Apolo Apolo, Orly Enrique
This study evaluated the use of visible and near-infrared (Vis-NIR) spectroscopy combined with machine learning (ML) algorithms to predict soil fertility-related properties in two contrasting agroecological regions of Peru: the Highlands and the Rainforest. A total of 297 soil samples were analyzed using portable spectroradiometers covering a spectral range of 350–2500 nm, applying transformations such as Savitzky–Golay smoothing, first derivative, and band depth. Predictive models were developed using PLSR, Random Forest, Support Vector Machines, and neural networks. Results show variable predictive performance across soil properties and ecosystems. Organic matter in Highland soils and calcium in Rainforest soils achieved the strongest test-set accuracy (R2 > 0.70), while pH and texture fractions showed moderate performance (R2 = 0.42–0.67), and mobile nutrients including phosphorus, potassium, and sodium showed limited predictive accuracy due to their weak spectral expression. Spectral predictions were further integrated into a structured nutrient balance framework to assess agronomic reliability. Nitrogen fertilizer recommendations showed the strongest agreement between observed and predicted values across both ecosystems, whereas K2O and CaO recommendations in Highland soils were substantially underestimated, demonstrating that property-level statistical performance does not guarantee agronomic reliability. These findings confirm that Vis-NIR spectroscopy combined with ML represents a fast, cost-effective, and sustainable alternative to conventional soil analysis, especially in rural areas with limited laboratory infrastructure. Expanding regional calibration datasets and exploring mid-infrared FTIR spectroscopy as a complementary technology are identified as priority directions for improving predictions of agronomically critical nutrients.