Examinando por Autor "De Mendiburu, Felipe"

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From rangelands to cropland, land-use change and its impact on soil organic carbon variables in a Peruvian Andean highlands: a machine learning modeling approach
(Springer, 2024-09-09) Carbajal, Mariella; Ramirez, David A.; Turin Canchaya, Cecilia Claudia; Schaeffer, Sean M.; Konkel, Julie; Ninanya, Johan; Rinza, Javier; De Mendiburu, Felipe; Zorogastua, Percy; Villaorduña, Liliana; Quiroz, Roberto
Andean highland soils contain significant quantities of soil organic carbon (SOC); however, more efforts still need to be made to understand the processes behind the accumulation and persistence of SOC and its fractions. This study modeled SOC variables—SOC, refractory SOC (RSOC), and the 13C isotope composition of SOC (d13CSOC)—using machine learning (ML) algorithms in the Central Andean Highlands of Peru, where grasslands and wetlands (‘‘bofedales’’) dominate the landscape surrounded by Junin National Reserve. A total of 198 soil samples (0.3 m depth) were collected to assess SOC variables. Four ML algorithms—random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and eXtreme gradient boosting (XGB)—were used to model SOC variablesusing remote sensing data, land-use and landcover (LULC, nine categories), climate topography, and sampled physical–chemical soil variables. RF was the best algorithm for SOC and d13CSOC prediction, whereas ANN was the best to model RSOC. ‘‘Bofedales’’ showed 2–3 times greater SOC (11.2 ± 1.60%) and RSOC (1.10 ± 0.23%) and more depleted d13CSOC (- 27.0 ± 0.44 &) than other LULC, which reflects high C persistent, turnover rates, and plant productivity. This highlights the importance of ‘‘bofedales’’ as SOC reservoirs. LULC and vegetation indices close to the near-infrared bands were the most critical environmental predictors to model C variables SOC and d13CSOC. In contrast, climatic indices were more important environmental predictors for RSOC. This study’s outcomes suggest the potential of ML methods, with a particular emphasis on RF, for mapping SOC and its fractions in the Andean highlands.
Unraveling ecophysiological mechanisms in potatoes under different irrigation methods: a preliminary field evaluation
(MDPI, 2020-06-11) Silva Díaz, Cecilia; Ramírez, David A.; Rodríguez Delfín, Alfredo; De Mendiburu, Felipe; Rinza, Javier; Ninanya, Johan; Loayza, Hildo; Quiroz, Roberto
Potatoes—a global food security and staple crop—is threatened by dry spells in drought-prone areas. The use of physiological thresholds to save water while maintaining a reasonable tuber yield has been proposed, but their effects on physiological performances and usefulness under different irrigation methods are yet to be evaluated. In this study, photosynthetic traits were monitored to assess the effect of water restriction and rewatering under drip (DI) and furrow (FI) irrigations. The treatments consisted of two maximum light-saturated stomatal conductance (g𝑠_𝑚𝑎𝑥) irrigation thresholds (T2: 0.15 and T3: 0.05 mol H2O m−2 s−1) compared with a fully irrigated control (g𝑠_𝑚𝑎𝑥 > 0.3 mol H2O m−2 s−1). DI used less water than FI but promoted early senescence and low percentage of maximum assimilation rate (PMA) at late developmental stages. FI caused no yield penalization in T2 and higher recovery of carbon isotope discrimination and PMA than DI. It is suggested that moderate water quantities of early and frequently water pulses in the irrigation, promote short-term water stress memory improvement, senescence delay and more capability of recovery at late stages.
Water saving using thermal imagery-based thresholds for timing irrigation in potatoes under drip and furrow irrigation systems
(MDPI, 2022-11-23) Rinza, Javier; Ramírez, David A.; Ninanya, Johan; De Mendiburu, Felipe; García, Jerónimo; Quiroz, Roberto
Under the current water crisis in agriculture, irrigation methods for saving and conserving water are necessary. However, these methods must guarantee an appropriate yield with a concomitant economic benefit and a reduced environmental impact. In this study, two irrigation thresholds for irrigation timing (IT) based on thermal imagery were analyzed with the UNICA potato variety in three trials under drip (DI) and furrow (FI) irrigation during 2017–2018 in Lima, Peru. The control (T1) remained at >70% of soil field capacity. For other treatments, thresholds were defined based on stomatal conductance at light saturation (T2: 0.15 and T3: 0.05 mol H2O m−2 s−1) and crop water stress index (T2: 0.4 and T3: 0.6) based on canopy temperature. An integrated index (IIN) was established for the valuation of treatments using the criteria of high fresh tuber yield (FTY) and a low total amount of irrigated water, production cost (PC), and total C emissions (TE) and using criteria of a score. FI-T2 (0.69–0.72) and DI-T3 (0.19–0.29) showed the highest and lowest IIN value, respectively. FTY in T2 was not significantly reduced under FI, resulting in a lower PC regarding DI–T2 and emphasizing the usefulness of thermal imagery in determining watering schedules in potatoes under furrow irrigation systems.