Estimating Sugarcane Yield in a Subtropical Climate Using Climatic Variables and Soil Water Storage

Abstract

Brazil is the largest producer of sugarcane (Saccharum spp.) in the world, and this crop’s response to climate and soil water storage is essential for optimal management and genetic/yield improvements. The objective of our study was to build a multivariate model to estimate sugarcane yield in the subtropical conditions of the northwestern Paraná region using climatic and soil water storage variables. Observed yield data was used from experiments conducted at the Experimental Station of the Sugarcane Genetic Improvement Program of the Universidade Federal do Paraná. The sugarcane varieties RB72454, RB867515, RB966928, and RB036066 were analyzed in the 1998–2006, 2008, 2018 and 2019 harvest years. Stepwise multiple linear regression analysis with repeated cross-validation was developed to estimate sugarcane yield given climate and soil water storage variables for crop growth phases. The accumulated degree days in Phases I and II and soil water storage in Phase II of development significantly impacted sugarcane yield. The multiple linear regression model, with accumulated degree days and soil water storage in Phases I and II of development, successfully predicted sugarcane yield for analyzed varieties. Sugarcane production models like the one we developed can improve crop management for greater sustainability and climate change adaption in Brazil and other areas.

Water Dynamics and Hydraulic Functions in Sandy Soils: Limitations to Sugarcane Cultivation in Southern Brazil

“…The highest AWC2 and EAW2 occurred in the surface layer (p < 0.05) due to the higher organic carbon content that increases water retention and availability for plants [38]. This surface layer corresponds to the soil layer with the highest volume and density of root length for sugarcane [34]. Figure 4 shows the variation in the percentage of water availability for the soil in the experimental area at the Experimental Station of the Sugarcane Genetic Improvement Program at the Federal University of Paraná and the Inter-University Network for the Development of Sugarcane Energy.…”

“…In the simulation, the logarithmic transformation of the multiple linear regression model was performed: TCH = e −11.3374 × ADD I −0.8224 × SWS II 10.5272 (28) where TCH is metric tons of sugarcane stalk per hectare, ADD I is growing degree days ( • C) during the first stage of sugarcane crop development (July through October), and SWS II is soil water storage during the second development (October through March) for a 12-month production period. The third stage of development runs from March through July where the crop accumulates sucrose [34]. Simulated yields (y-axis) were then graphed three-dimensionally against SWS II (AWC and EAW) measured in cm (x-axis) and ADD I measured in • C (z-axis) for all data points.…”

Predicting Sugarcane Yield via the Use of an Improved Least Squares Support Vector Machine and Water Cycle Optimization Model

“…This has promoted the development of sustainable economy. China ranks third in global sugarcane production behind Brazil and India [6]. Among them, the southern provinces of Guangxi, Hainan, Guangdong, and Yunnan are the main producing regions, which have similar climatic characteristics: strong solar radiation, abundant rainfall, and high average temperatures.…”

Sustainable Development Agricultural Economics and Policy: Intensification versus Diversification

“…For example, biophysical modeling can be used to evaluate and improve sustainability. In Brazil, use of growth-stage specific regression modeling can identify factors that limit sugarcane yield such as soil water storage during the second growth phase in sandy soils [12]. Agricultural erosion modeling using GeoWEPP for crops, pasture, and natural habitat in Brazil's Midwest can be validated and used to help minimize erosion at the micro-watershed scale [13].…”