Crop Parameters for Modeling Sugarcane under Rainfed Conditions in Mexico

Baez-González, Alma Delia; Kiniry, James R.; Meki, Manyowa N.; Williams, J. R.; Alvarez-Cilva, Marcelino; Ramos-Gonzalez, Jose L.; Magallanes-Estala, Agustin; Zapata-Buenfil, Gonzalo

doi:10.3390/su9081337

Cited by 18 publications

(11 citation statements)

References 44 publications

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“…In this study, a default value of 0.65 is set according to Baez‐Gonzalez et al. (2017), Inman‐Bamber and Thompson (1989), and Inman‐Bamber (1991). However, the value of K i often varies because of different sugarcane varieties and different development stages and stress (Campillo et al., 2012).…”

Section: Discussionmentioning

confidence: 99%

“…where fPAR is the fraction of PAR intercepted by the plant canopy, and K i is the light extinction coefficient that depends on crop variety and phenology, which is set to 0.65 in this study (Baez‐Gonzalez et al., 2017; Inman‐Bamber, 1991; Inman‐Bamber & Thompson, 1989). The influence of this coefficient is discussed in Subsection 4.3.…”

Section: Data Collection and Model Developmentmentioning

confidence: 99%

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A new sugarcane yield model using the SiPAR model

Shi

Huang³

2021

Agronomy Journal

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Physical process–based crop yield models are subject to extensive input requirements, and traditional statistical models often lack robustness in a changing environment. The purpose of this study was to develop a new and simple semi‐physical sugarcane (Saccharum officinarum L.) yield model, called the SiPAR model (intercepted photosynthetically active radiation partitioned to stem), with less data requirement than the process‐based models and strong robustness. The SiPAR model was developed using the normalized difference vegetation index, the leaf area index, and solar radiation data. A 3‐yr field experiment was used to evaluate model performance. The SiPAR model was also compared with three traditional statistical models. The results showed that (a) the SiPAR model obtained the highest accuracy among the compared methods and reproduced the spatial pattern of yield well (RMSE = 5.88–8.65 t ha–1; R2 = .44–.87; normalized RMSE (Ry) = 7.22–11.77%) and (b) the SiPAR model had better spatial and temporal stability than the other three statistical models through cross‐validation because the SiPAR model considered the stem biomass accumulation features of sugarcane by introducing a weighting factor to reflect the stem potential growth rate and using intercepted photosynthetically active radiation to represent the energy available for photosynthesis. The SiPAR model has the potential to be applied at a regional scale because it requires less data and fewer parameters and is robust and highly accurate.

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Section: Discussionmentioning

confidence: 99%

Section: Data Collection and Model Developmentmentioning

confidence: 99%

A new sugarcane yield model using the SiPAR model

Shi

Huang³

2021

Agronomy Journal

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“…In preliminary studies, the model was parameterized to evaluate the performance of cultivar CP72 2086 in three regions of Mexico (Baez‐Gonzalez et al., ). Field data from thirty dryland sugarcane locations in Northeastern Mexico, Gulf of Mexico and Pacific Mexico with distinct soil and climatic conditions were used to calibrate and validate the model.…”

Section: Methodsmentioning

confidence: 99%

Potential impact of future climate change on sugarcane under dryland conditions in Mexico

Baez-González

Kiniry

Meki

et al. 2018

J Agronomy Crop Science

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Assessments of impacts of future climate change on widely grown sugarcane varieties can guide decision‐making and help ensure the economic stability of numerous rural households. This study assessed the potential impact of future climatic change on sugarcane grown under dryland conditions in Mexico and identified key climate factors influencing yield. The Agricultural Land Management Alternatives with Numerical Assessment Criteria (ALMANAC) model was used to simulate sugarcane growth and yield under current and future climate conditions. Management, soil and climate data from farm sites in Jalisco (Pacific Mexico) and San Luis Potosi (Northeastern Mexico) were used to simulate baseline yields. Baseline climate was developed with 30‐year historical data from weather stations close to the sites. Future climate for three decadal periods (2021–2050) was constructed by adding forecasted climate values from downscaled outputs of global circulation models to baseline values. Climate change impacts were assessed by comparing baseline yields with those in future decades under the A2 scenario. Results indicate positive impacts of future climate change on sugarcane yields in the two regions, with increases of 1%–13% (0.6–8.0 Mg/ha). As seen in the multiple correlation analysis, evapotranspiration explains 77% of the future sugarcane yield in the Pacific Region, while evapotranspiration and number of water and temperature stress days account for 97% of the future yield in the Northeastern Region. The midsummer drought (canicula) in the Pacific Region is expected to be more intense and will reduce above‐ground biomass by 5%–13% (0.5–1.7 Mg/ha) in July–August. Harvest may be advanced by 1–2 months in the two regions to achieve increases in yield and avoid early flowering that could cause sucrose loss of 0.49 Mg ha−1 month−1. Integrating the simulation of pest and diseases under climate change in crop modelling may help fine‐tune yield forecasting.

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“…The population structure analysis in 97 elite and historic sugarcane varieties by using 6,534 InDel and SNP markers revealed the strong structural differentiation into two major subgroups and a stronger marker-trait correlation with sucrose traits (Fickett et al, 2019 ). Sugarcane crop is a photosensitive crop which requires specific environmental regimes for plant developmental activities (Baez-Gonzalez et al, 2017 ). The crop growth and developments differ with agro-climatic zones such as tropical and subtropical regions.…”

Section: Genomic Selection In Sugarcanementioning

confidence: 99%

Genomic Selection in Sugarcane: Current Status and Future Prospects

et al. 2021

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Sugarcane is a C4 and agro-industry-based crop with a high potential for biomass production. It serves as raw material for the production of sugar, ethanol, and electricity. Modern sugarcane varieties are derived from the interspecific and intergeneric hybridization between Saccharum officinarum, Saccharum spontaneum, and other wild relatives. Sugarcane breeding programmes are broadly categorized into germplasm collection and characterization, pre-breeding and genetic base-broadening, and varietal development programmes. The varietal identification through the classic breeding programme requires a minimum of 12–14 years. The precise phenotyping in sugarcane is extremely tedious due to the high propensity of lodging and suckering owing to the influence of environmental factors and crop management practices. This kind of phenotyping requires data from both plant crop and ratoon experiments conducted over locations and seasons. In this review, we explored the feasibility of genomic selection schemes for various breeding programmes in sugarcane. The genetic diversity analysis using genome-wide markers helps in the formation of core set germplasm representing the total genomic diversity present in the Saccharum gene bank. The genome-wide association studies and genomic prediction in the Saccharum gene bank are helpful to identify the complete genomic resources for cane yield, commercial cane sugar, tolerances to biotic and abiotic stresses, and other agronomic traits. The implementation of genomic selection in pre-breeding, genetic base-broadening programmes assist in precise introgression of specific genes and recurrent selection schemes enhance the higher frequency of favorable alleles in the population with a considerable reduction in breeding cycles and population size. The integration of environmental covariates and genomic prediction in multi-environment trials assists in the prediction of varietal performance for different agro-climatic zones. This review also directed its focus on enhancing the genetic gain over time, cost, and resource allocation at various stages of breeding programmes.

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Crop Parameters for Modeling Sugarcane under Rainfed Conditions in Mexico

Cited by 18 publications

References 44 publications

A new sugarcane yield model using the SiPAR model

A new sugarcane yield model using the SiPAR model

Potential impact of future climate change on sugarcane under dryland conditions in Mexico

Genomic Selection in Sugarcane: Current Status and Future Prospects

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