Adjustment of water-crop production models for ratoon sugarcane

Souza, Jorge Luiz Moretti de; Gerstemberger, Emerson; Gurski, Bruno César; Oliveira, Ricardo Augusto de

doi:10.1590/1983-40632015v4537687

Cited by 7 publications

(5 citation statements)

References 16 publications

(15 reference statements)

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“…In a study on sugar cane in Brazil, the λ coefficient was calculated for germination to tillering, development and growth reduction (sucrose accumulation). The λ coefficient at the growth stage mentioned was estimated to be 0.43, 0.39 and 0.07, respectively (Souza et al, 2015). The results of the study mentioned showed that sugar cane sensitivity in the germination to the tillering stage was greater than that in other growth stages.…”

Section: Resultsmentioning

confidence: 99%

“…The mentioned models provide good estimations of crop yield, and they optimize irrigation water management during the growing season (Chen et al, 2014). Water stress sensitivity coefficients in the Jensen model were estimated for maize growth stages in Belgium (Kipkorir & Raes, 2002), sorghum in the United States (Jensen, 1968) and sugar cane in Brazil (Souza et al, 2015).…”

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confidence: 99%

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Modelling the relationships between the yield and evapotranspiration of maize under salinity stress and nitrogen deficiency

Saeidi

Sotoodehnia

Etedali

2022

Irrigation and Drainage

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Understanding the intraseasonal sensitivity of crop evapotranspiration (ET) to stresses allows for accurate modelling of crop yield. In this research, the effects of water salinity stress and soil nitrogen deficiency on evapotranspiration (in the growth stages) and maize yield in 2017 and 2018 were investigated.Four treatments of saline water levels of 0.5 (S 0 ), 2.1 (S 1 ), 3.5 (S 2 ) and 5.7 (S 3 ) dS m À1 were combined with four nitrogen fertilization levels of 100% (N 0 ), 75% (N 1 ), 50% (N 2 ) and 25% (N 3 ). The maize ET amounts from the S 0 N 0 to S 3 N 3 treatments decreased from 80 to 55 mm (in the initial stage), 202 to 129 mm (in development), 127 to 80 mm (in the mid) and 14.5 to 10.5 mm (in the late). The dry biomass yield of maize from the S 0 N 0 to S 3 N 3 treatments was equal to 15.3 to 9.23 t.ha À1 . The relative yield of maize was modelled by the additive models of Blank, Stewart and Singh and the multipliable models of Jensen, Rao and Minhas. The results showed that the Stewart and Jensen models were selected as the optimal models. By using the optimal models, the relative yield of maize can be modelled based on the evapotranspiration rate (in the growth stages) under salinity stress and nitrogen deficiency.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Modelling the relationships between the yield and evapotranspiration of maize under salinity stress and nitrogen deficiency

Saeidi

Sotoodehnia

Etedali

2022

Irrigation and Drainage

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“…Doorenbos and Kassam (1994), in which all water, nutritional and phytosanitary needs are addressed. The potential productivity penalization as a function of water deficit was performed using Jensen equation (Jensen, 1968;Souza et al, 2013;Souza et al, 2015):…”

Section: Methodsmentioning

confidence: 99%

Yield components and crop estimation for wheat cultivars at different sowing times

et al. 2020

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The objective of this study was to evaluate the yield and yield components of wheat cultivars, and compare the productivity obtained in the field and estimated with Jensen model, at different sowing dates, in Cascavel and Palotina cities, Parana state, Brazil. The experiments were carried out in the field at the COODETEC Research Center, located in Cascavel and Palotina. The experiment was conducted in a randomized blocks design in a 7 × 3 factorial, with plots consisting of six lines spaced at 5 × 0.17 meters, with seven wheat cultivars and three sowing dates, with three repetitions. The pluvial precipitation higher than crop evapotranspiration in all cultivation cycles analyzed did not prevent water deficiencies, which occurred predominantly in the heading and physiological maturation stages. The smallest water deficiencies in Cascavel (31.2 mm; April 22) and Palotina (7.2 mm; March 31), from sowing to heading, reduced grain yield by 41.7% and 42.8%, respectively, in relation to the higher productivity of each location. Small differences in yield components (NEM, NGE and MMG) provide differences in grain yield between the cultivars tested. The sowing carried out at the end of April and in May tended to have a higher yield. The best performances with Jensen model to estimate wheat grains yield were obtained for CD 1440 and QUARTZO cultivars. The Jensen model overestimated the grain yield of the CD 108, CD 1104, CD 150 and CD 154 cultivars, since the model do not consider losses due to excessive rainfall and/or pests and diseases.

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“…In general, the soil of the experimental area cultivated with sugarcane showed low water availability for plants in the layers analyzed (Table 6), which is characteristic of soils with low clay contents [5,37]. There was no statistical difference between the layers (p > 0.05), but the highest AWC 1 and EAW 1 occur in the soil layers at 0.2 to 0.4 m and 0.4 to 0.6 m depth due to the higher amount of clay and microporosity [5].…”

Section: Water Availabilitymentioning

confidence: 99%

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

et al. 2023

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Crop cultivation on sandy soils is susceptible to water stress. Therefore, we determined the physical-hydric attributes of a Latossolo Vermelho distrófico (Oxisol) in northwestern Paraná state, Brazil. Soil samples were collected at depth ranges of 0 to 0.2 m, 0.2 to 0.4 m, and 0.4 to 0.6 m. We measured clay, silt, sand, fine and coarse sand contents, soil particle density, soil bulk density, total porosity, microporosity, and macroporosity. We also measured soil characteristics such as saturated and unsaturated soil hydraulic conductivities, pore distribution, water retention, available water capacity, and easily available water. We also estimated soil moisture, matric potential at field capacity, and time at field capacity. Validation of associations among these soil physical-hydric attributes was performed using principal component analysis. For the sandy soils analyzed, the distributions of coarse and fine sand fractions were measured for better evaluation of the soil’s physical and hydric attributes. Higher coarse sand contents increased soil hydraulic conductivities, maximum pore diameter, and macroporosity while reducing microporosity. Fine sand content reduced conductivity and increased soil water retention in subsurface layers. Simulated sugarcane yield increased with soil water storage. These results support improving crop simulation modeling of sugarcane to support sustainable intensification in regions with sandy soils.

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Adjustment of water-crop production models for ratoon sugarcane

Cited by 7 publications

References 16 publications

Modelling the relationships between the yield and evapotranspiration of maize under salinity stress and nitrogen deficiency

Modelling the relationships between the yield and evapotranspiration of maize under salinity stress and nitrogen deficiency

Yield components and crop estimation for wheat cultivars at different sowing times

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

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