Use of growing degree indicator for developing adaptive responses: A case study of cotton in Florida

Sharma, Anjali; Deepa, R.; Sankar, Sriramana; Pryor, Mikela; Stewart, Briyana; Johnson, Elijah; Anandhi, Aavudai

doi:10.1016/j.ecolind.2021.107383

Cited by 17 publications

(16 citation statements)

References 53 publications

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“…The use of ATU helped to describe the duration of phenological phases of quinoa, which can be useful to manage the crop in all phases best, guiding the time for fertilization, plant protection, irrigation and in genotype selection to originate different maturity groupings (Anandhi, 2016). Moreover, predictability ATU of phenological phases can direct selection in quinoa to face climate changes (Sharma et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

“…Thermal units can be a tool to determine the sowing and harvest time of crops affected by climate change. The rise in temperature speeds up the phases; therefore, using the number of days alone has been no longer valid to assess growth and development phases (Sharma et al, 2021). Variations in air temperature can anticipate the phenological phases, turning the events of crop growth less unpredictable when measured in the number of days (Souza et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Cálculo del tiempo térmico para 13 códigos de la escala BBCH de 12 progenies de quinua en las condiciones de crecimiento de la Sabana Brasileña

Jojoa

Peixoto

Spehar

et al. 2021

Rev. Colomb. Cienc. Hortic.

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La introducción de la quinua (Chenopodium quinoa Willd.) en la Sabana Brasileña ha tenido éxito basada en la selección de progenies de los tipos de valle. Dada la amplia variación de ambientes, una alternativa para definir el ciclo de madurez de la planta ha sido el uso del tiempo térmico acumulado (TTA). Esta medida permite la predicción del ciclo de la planta y proporciona apoyo para definir la duración de la fenología, siendo útil en el manejo y mejoramiento de los cultivos. Este estudio tuvo como objetivo calcular las unidades térmicas para 13 códigos de la escala BBCH de la quinua, mediante la evaluación de 12 progenies seleccionadas y cultivadas en dos fechas de siembra, a 15° 56' S y 47° 55' O, altitud de 1.100 m, Brasilia, DF, Brasil. Las diferencias estadísticas fueron predominantes desde el inicio de las fases reproductivas BBCH-50, clasificando las progenies como precoces, de ciclo medio y tardías. Las progenies de madurez temprana y sus respectivas TTA para BBCH-89 son BRQ4 (1.676,8), BRQ1 (1.685) y AUR (1.691), contrastando con las tardías BLA (2.239), BRQ3 (1.929,1 GDD) y BRQ8 (1.895). Las unidades térmicas acumuladas para BBCH-89 oscilaron entre 1.565,25 y 2.381, con una diferencia entre los genotipos más precoces y los más tardíos de 815,75. Las progenies seleccionadas de los cultivares existentes son diferentes en cuanto a la acumulación de unidades térmicas, lo que implica la eficiencia en la adquisición de cultivares para escalonar el cultivo de quinua. Las unidades térmicas acumuladas explican el rango de ciclos de maduración de las plantas en la selección. Además, el cálculo del TTA para los códigos de la escala BBCH es una herramienta eficaz para predecir el ciclo fenológico de la quinua.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cálculo del tiempo térmico para 13 códigos de la escala BBCH de 12 progenies de quinua en las condiciones de crecimiento de la Sabana Brasileña

Jojoa

Peixoto

Spehar

et al. 2021

Rev. Colomb. Cienc. Hortic.

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“…Specifically, the accumulated vegetation indices were calculated using the cumulative integral of the time-series curve, whereas the accumulated weather features were calculated by summing the daily values over the last 7 days. Growing degree days, which capture the effective growth time of the plant, were also calculated according to (3) [27]. T base refers to the temperature under which the cotton does not develop and is equal to 15.6 • C, and T max , T min are the maximum & minimum air temperatures (2 m), respectively.…”

Section: Feature Engineeringmentioning

confidence: 99%

Pest presence prediction using interpretable machine learning

Ornela¹,

Sitokonstantinou²,

Tsoumas³

et al. 2022

Preprint

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Helicoverpa Armigera, or cotton bollworm, is a serious insect pest of cotton crops that threatens the yield and the quality of lint. The timely knowledge of the presence of the insects in the field is crucial for effective farm interventions. Meteo-climatic and vegetation conditions have been identified as key drivers of crop pest abundance. In this work, we applied an interpretable classifier, i.e., Explainable Boosting Machine, which uses earth observation vegetation indices, numerical weather predictions and insect trap catches to predict the onset of bollworm harmfulness in cotton fields in Greece. The glass-box nature of our approach provides significant insight on the main drivers of the model and the interactions among them. Model interpretability adds to the trustworthiness of our approach and therefore its potential for rapid uptake and context-based implementation in operational farm management scenarios. Our results are satisfactory and the importance of drivers, through our analysis on global and local explainability, is in accordance with the literature.

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“…A simple, yet efficient method to assess crop performance is the Growing Degree Days (GDD) (Hassan et al, 2007). GDD represent the amount of heat required by the crop in a defined time period (Cleland et al, 2007) and can be calculated based on equation 1 (Sharma et al, 2021).…”

Section: Academicpresmentioning

confidence: 99%

Cotton versus climate change: the case of Greek cotton production

Engonopoulos

Kouneli

Mavroeidis

et al. 2021

Not Bot Horti Agrobo

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Through the last century, the increased greenhouse gases emissions altered the atmosphere’s composition and resulted to the phenomenon known as climate change. Climate change threatens the sustainability of the agricultural sector in the Mediterranean region. Droughts and extreme heat waves will probably become more frequent in the next few decades, thus maintaining sufficient yields in heat and drought susceptible major crops will be challenging. In Greece, cotton is of paramount economic importance. Besides the fact that it is regarded as the most significant fiber crop, Greece is the main cotton producer of the European Union. The aim of the present review was to examine the environmental factors that might affect cotton production in Greece and assess whether (or not) climate change has the potential to limit the productivity of this crop in the near future. According to the existing literature, cotton can adapt to the changing climate. Climate change-induced elevated CO2 levels and temperatures might even benefit cotton. The mitigation of the adverse effects of climate change is possible via the adaptation of site-specific agronomic practices. A simplistic framework, based on the literature and the goals of the European Union, that aims to the preservation of sufficient cotton yields in Greece is proposed in the present study.

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Use of growing degree indicator for developing adaptive responses: A case study of cotton in Florida

Cited by 17 publications

References 53 publications

Cálculo del tiempo térmico para 13 códigos de la escala BBCH de 12 progenies de quinua en las condiciones de crecimiento de la Sabana Brasileña

Cálculo del tiempo térmico para 13 códigos de la escala BBCH de 12 progenies de quinua en las condiciones de crecimiento de la Sabana Brasileña

Pest presence prediction using interpretable machine learning

Cotton versus climate change: the case of Greek cotton production

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