Influence of morpho-physiological traits on root yield in sweet potato (Ipomoea batatas Lam.) genotypes and its adaptation in a sub-humid environment

Pérez-Pazos, Jazmín Vanessa; Rosero, Amparo; Martínez, Remberto; Pérez, José Luis; Morelo, Julio; Araújo, H. M. de; Burbano-Erazo, Esteban

doi:10.1016/j.scienta.2020.109703

Cited by 12 publications

(3 citation statements)

References 41 publications

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“…Moreover, it is unclear whether existing forecasting frameworks that work effectively for crops like wheat, corn and soybean will transfer to other crops with distinct physiology, like roots and tubers. Although roots and tubers grow underground, studies suggest a relationship between yield and above-ground traits, such as vegetation indices or canopy cover, particularly during vegetative growth and around tuber or storage root initiation (Tedesco et al, 2021;Pérez-Pazos et al, 2021;Sun et al, 2020). The relationship between yields and above-ground plant characteristics indicates that remotely sensed data may still be useful when predicting root and tuber yield.…”

Section: Introductionmentioning

confidence: 99%

In-season Sweetpotato Yield Forecasting using Multitemporal Remote Sensing Environmental Observations and Machine Learning

Carbajal-Carrasco,

Jones,

Williams

et al. 2024

Preprint

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Data-driven modeling approaches for crop yield prediction have exponentially increased in the last decade due to the greater availability of spatial data from various sensors. Yet, most yield modeling has focused on major commodities, leaving lesser-cultivated horticultural crops like sweetpotato relatively undertooled, though these crops considerably contribute to the global economy and food supply. The U.S. is the primary exporter of sweetpotato (271 K tonnes), with 21% of U.S.-grown sweetpotatoes being exported. Early yield forecasting at the county scale offers crucial insights for growers, packers, wholesalers, and associated industries, enabling them to anticipate variations in yield to make informed decisions. While roots and tubers have demonstrated a relationship between yields and above-ground plant characteristics, it remains uncertain whether forecasting models that utilize remotely sensed data, including vegetation indices, are suitable for sweetpotato. We developed county-scale in-season sweetpotato yield forecast models using machine learning (ML) algorithms and multitemporal remote sensing environmental data. Four of the most commonly used ML algorithms for predicting crop yield -Random Forest Regression (RFR), Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) -were applied using stationary (topography and soil characteristics), and temporal (weather, NDVI, and Growing Degree Days) variables as potential predictors. Six predictor sets were tested to identify key predictor variables, optimal aggregation time (16 or 32 days composite) of the temporal variables, and how early in the growing season the models can reliably predict end-of-season yields. U.S. Annual CropScape land cover layers were used to identify sweetpotato fields, over which temporal variables were aggregated, and sweetpotato yields were tabulated from the USDA Agricultural Survey from 2008 to 2022. The Boruta method was used for feature selection across each predictor set before training the ML models.RFR outperformed other ML algorithms and the RFR models' evaluation metrics were the most consistent across the six predictor sets. The RFR model that incorporated early and mid season temporal variables as 16-day composites was selected and proposed for future sweetpotato yield forecasting due to its performance (R 2 = 0.44, RMSE = 3.53 tonnes.ha -1 ), as well as ability to predict early enough 1 in the season to provide actionable information. In the final model, several stationary variables (elevation, nitrogen, cec, soc, and clay content) were the most predictive of sweetpotato yield. After these stationary variables, NDVI and precipitation from the time around storage root initiation and bulking (July), and minimum temperature around planting (June) followed in importance.

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Section: Introductionmentioning

confidence: 99%

In-season Sweetpotato Yield Forecasting using Multitemporal Remote Sensing Environmental Observations and Machine Learning

Carbajal-Carrasco,

Jones,

Williams

et al. 2024

Preprint

View full text Add to dashboard Cite

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“…Esta adaptación a ambientes contrastantes garantiza su fomento como cultivo comercial en diferentes zonas agroecológicas con diferente calidad y cantidad de radiación, que influyen en la eficiencia fotosintética (Adebola et al, 2013), y su capacidad de producir raíces tuberosas. Genotipos con alta eficiencia de cobertura del suelo a través de la producción de biomasa foliar regulan su tasa fotosintética y parámetros asociados con el uso eficiente de agua, aspectos que benefician la habilidad de formar raíces tuberosas incluso en condiciones de estrés por alta temperatura y baja disponibilidad de agua (Burbano-Erazo et al, 2020;Pérez-Pazos et al, 2021).…”

Section: Introductionunclassified

Evaluación agronómica de germoplasma promisorio de batata (Ipomoea batatas) en el departamento del Valle del Cauca, Colombia

2022

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La batata (Ipomoea batatas L.) es una especie de importancia económica, ya que contribuye a la seguridad alimentaria de millones de personas. Sin embargo, en Colombia esta especie es subutilizada, cultivándose de forma artesanal en la región Caribe. El objetivo de este estudio fue evaluar las características agronómicas de germoplasma de batata, bajo las condiciones agroambientales del departamento del Valle del Cauca, Colombia. Se sembraron 29 genotipos usando un diseño de bloques completos al azar y 27 genotipos, para su uso como parentales en una parcela única y evaluar la producción de semilla sexual para procesos de recombinación genética. Se encontró una respuesta diferencial entre los genotipos evaluados, sobresaliendo el genotipo 1115-200_BOL con un rendimiento de raíces tuberosas totales y de primera categoría de 64.9 t ha-1 y 52.7 t ha-1 respectivamente, y un porcentaje de raíces tuberosas de primera categoría del 81 %. En la producción de estructuras reproductivas, los genotipos que se destacaron fueron 0615-070_COR, 1215-227_BOL y la variedad Agrosavia Aurora. Todas estas características indicaron es posible establecer procesos de mejoramiento y la inserción de la batata como cultivo, bajo las condiciones agroambientales del Valle del Cauca. Además, se identificaron como posibles cultivares comerciales los genotipos 1115-200_BOL y 1215-232_SUC para esta región de Colombia.

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“…In sweet potato, the growth rate decreased between 45 and 75 days after planting, which may be related to tuberization and secondary vine onset. Later, a significant increase in growth rate was noticed till 90 days after planting, which may be associated with the efficient accumulation of biomass in tubers (P´erez-Pazos et al, 2021). But information is lacking on source-sink transition, partitioning of dry matter and metabolites to various organs during different growth stages, rhizome bulking process and the relationship between photosynthesis and rhizome development etc.…”

Section: Introductionmentioning

confidence: 99%

Source sink relationship, dry matter and starch partitioning in developing ginger rhizomes during different growth stages

Krishnamurthy

Kandiannan²

2021

JPC

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Source sink relationship, dry matter and starch partitioning, rhizome bulking process in relation to dry matter and starch partitioning in developing rhizomes and growth and gas exchange parameters were studied in three popular varieties of ginger viz., IISR Varada, IISR Mahima and IISR Rejatha. Results revealed that maximum tiller production and leaf area accumulation occurred between 60 and 120 days after planting (DAP) in all three varieties. Photosynthetic rate and hormone contents (auxin and cytokinin) increased from 90-120 DAP, peaked at 120 DAP and then started declining. Biomass partitioning data revealed that the active biomass accumulation stage was between 60 and 150 DAP in ginger. The dry matter accumulation pattern in rhizomes also revealed that maximum dry matter accumulation in rhizomes also occurred between 60 and 150 DAP in all the three varieties. Maximum starch accumulation in the rhizomes also occurred during the same period. These results suggest that most of the rhizome bulking process occurred between 60 and150 DAP in ginger. Total biomass accumulation, dry matter accumulation and starch accumulation in rhizomes followed similar trends.

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Influence of morpho-physiological traits on root yield in sweet potato (Ipomoea batatas Lam.) genotypes and its adaptation in a sub-humid environment

Cited by 12 publications

References 41 publications

In-season Sweetpotato Yield Forecasting using Multitemporal Remote Sensing Environmental Observations and Machine Learning

In-season Sweetpotato Yield Forecasting using Multitemporal Remote Sensing Environmental Observations and Machine Learning

Evaluación agronómica de germoplasma promisorio de batata (Ipomoea batatas) en el departamento del Valle del Cauca, Colombia

Source sink relationship, dry matter and starch partitioning in developing ginger rhizomes during different growth stages

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