Estimation of the Retention and Availability of Water in Soils of the State of Santa Catarina

Abstract

Soil water retention and availability are important properties for agricultural production, which can be measured directly or estimated by pedotransfer functions. Some studies on this topic were carried out in Santa Catarina, Brazil. To improve the estimates, it is necessary to evaluate other properties, to analyze more soil types, as well as to use other analysis techniques such as artificial neural networks and regression trees. Thus, the objective of the study was to estimate the field capacity (FC), permanent wilting point (PWP), and available water (AW) in soils of Santa Catarina (SC), through multiple linear regressions (MLR), artificial neural networks (ANN), and regression trees (RT), more efficiently than the current pedotransfer functions. For this, samples of the horizons A and B of 70 profiles were collected to determine the texture, plasticity limit, FC, PWP, AW, specific surface (SS), organic carbon (OC) content, and microporosity. Pedotransfer functions were generated through MRL, ANN, and RT, considering as dependent variables the FC, PWP, and AW, and as independent variables the content of clay, silt, OC, plasticity limit, SS, and microporosity, through the test of four models, for surface and subsurface horizons. The RT estimated FC, PWP, and AW better than ANN and MRL. The best models to estimate water retention were those that used microporosity. When the database has few input variables, the model with clay, silt, and OC content is an alternative to estimate FC, PWP, and AW.

Soybean Agronomic Performance Does Not Change with Gypsum Application in a Cambisol Submitted to Water Restriction in Southern Brazil

“…The field capacity and permanent wilting point estimated values for the soil were 0.53 and 0.29 m 3 m -3 , respectively, which are within the range found in studies for the state of Santa Catarina [22,24]. It was observed that in both years, treatment with water restriction approached and remained partially below permanent wilting point (Figures 2a and b), with the 2019/20 agricultural year being more affected since its treatment without water restriction was compromised.…”

Modeling Soil Moisture from Multisource Data by Stepwise Multilinear Regression: An Application to the Chinese Loess Plateau

“…An SM data assimilation scheme is to simulate dynamic SM at spatiotemporal scales using estimated soil parameters and weather forcing based on a hydrological model [46,47]. The machine learning technique is computationally intensive (e.g., random forest (RF) [48,49], artificial neural network (ANN) [50][51][52][53], support vector regression (SVR) [54][55][56], and regression trees (RT) [57,58]) and used to build mathematical models based on training sets and covariates to extract SM information from the available data [59,60].…”

“…The SMLR method was also used to predict soil water infiltration in a dry flood plain of eastern Iran [71], to estimate the plant available water content of unsaturated soil [70]. When the variable of micro-porosity was included to estimate the availability of water in the soil, the SMLR (which features computation efficiency and ease of interpretation) was simpler to use compared with both ANN and RT [58].…”

“…The incomplete reference SM maps from our previously published, to some extent, were still not available for certain applications. Thus, using the SMLR method over the CLP to estimate SM makes the best possible use of all ancillary data (translating the available data into the required data), particularly data that are relatively inexpensive and easily accessible [58].…”

Available water content for the management of irrigated crops in the northwestern region of Parana State

“…Soil water retention prediction is increasing based on soil water stresses from pedotransfer functions by mineral and organic fractions of the soil (BARROS et al, 2013;MEDEIROS et al, 2014). It can be generated for soil water retention curve models (MEDEIROS et al, 2014), or specifically, for certain soil water tensions such as soil water content corresponding to field capacity and permanent wilt point (BORTOLINI; ALBUQUERQUE, 2018;FIDALSKI et al, 2013;MEDEIROS et al, 2014).…”