Probabilistic yield forecasting of robusta coffee at the farm scale using agroclimatic and remote sensing derived indices

Kouadio, Louis; Byrareddy, Vivekananda; Sawadogo, Alidou; Newlands, Nathaniel K.

doi:10.1016/j.agrformet.2021.108449

Cited by 17 publications

(8 citation statements)

References 44 publications

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“…Esto con el fin de lograr proyectar la cosecha y mejorar las herramientas de administración de fincas de café. Por lo que estos países deben desarrollar aplicaciones de agricultura inteligente para obtener mejores rendimientos de su finca como lo mencionan diversos autores [4][5][6][7].…”

Section: Resultsunclassified

“…Además, Minten et al, (2019) mostró un gran énfasis en el constante ataque de enfermedades, siendo enfermedades como la roya el factor determinante de producciones reducidas de café [2]. Las fincas que aplican diferentes métodos y tecnologías logran niveles más altos de producción que los productores de café convencionales [4].…”

Section: Introductionunclassified

“…Kittichotsatsawat et al, (2021) afirma que todas las redes de sensores inalámbricos de datos, computación en la nube, el internet de las cosas, procesamiento de imágenes, sensores remotos y tecnología de trazabilidad se utilizará en el suministro de café [7]. Esas grandes aplicaciones de datos se utilizaron para aumentar la producción y eficiencia en la gestión empresarial al servicio de las necesidades del cliente [4], [7]. Por lo que esta investigación tiene como objetivo identificar las diferentes tecnologías de la industria 4.0 que son utilizados en la agroindustria por los países lideres en producción de café.…”

Section: Introductionunclassified

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Proposed Open Systems Model and Intelligent Agriculture in Coffee Farms within the Harvesting Process

Ordonez-Avila¹,

Martínez-Rangel²

2022

Proceedings of the 2nd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 202

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The countries that produce the most coffee worldwide have an economy based on agribusiness and it is noteworthy that some of them apply various methods based on Industry 4.0 in their processes. However, in coffee production these technologies have a very limited application. The use of open systems makes it possible to talk about intelligent agriculture, which implements various technologies to improve some processes. This research work identifies the different technologies of Industry 4.0 that could be used in agriculture by the leading countries in coffee production as proposed in this research work. The development of the theoretical and methodological framework allows identifying three general steps in the management of information in this area: generation, grouping and visualization. After the theoretical review, the main trends of these countries with signs of intelligent agriculture are presented as results, given that they apply Industry 4.0 technologies. To visualize the impact that these technologies can have on coffee farms, the open systems model of Katz & Kan is proposed. The application of this model will allow the integration of Industry 4.0 within coffee farms. Finally, it is concluded that the most used technology is the internet of things and sensors, which many of the authors used together, while robotics is the least implemented. By implementing these technologies, the authors showed benefits in crop monitoring, irrigation systems and management systems.

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Section: Resultsunclassified

Section: Introductionunclassified

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Proposed Open Systems Model and Intelligent Agriculture in Coffee Farms within the Harvesting Process

Ordonez-Avila¹,

Martínez-Rangel²

2022

Proceedings of the 2nd LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD 202

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“…Based on the above result, it suggests that there was noticeable variation in the moisture levels across Thailand and Malaysia over 1 month, indicating a dynamic weather pattern that likely implies short-term shifting of climate [38,39]. Particularly in the 1990s, a 6-month analysis revealed a drying pattern likely due to El Niño, which strongly influences weather patterns in Southeast Asia [40,41]. In addition, using a mesoscale perspective could reduce the temporary oscillations within 1 month, providing a thorough portrayal of the season of water resources and agriculture [42] by revealing decreased volatility in temporal trends, including the impact of prolonged weather conditions on agricultural cultivation and harvesting [43].…”

Section: Spei In Southeast Asia During 1970-2019mentioning

confidence: 86%

Historical Analysis of the Effects of Drought on Rice and Maize Yields in Southeast Asia

Amnuaylojaroen,

Chanvichit

2024

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This study performs an empirical orthogonal function (EOF) analysis to study the relationship between rice and maize productivities and the different time scales of the Standardized Precipitation Evapotranspiration Index (SPEI), including 1-month, 6-month, and 12-month time scales in Southeast Asia during the years 1970–2019. For the 1-month time scale, the averaged SPEI values are in the range of −1.0 to 1.0, indicating a moderate degree of dryness and humidity. Both 6-month and 12-month time scales suggest that many countries in Southeast Asia are experiencing mostly wet conditions, with SPEI values reaching up to 1.5. The relationship analysis by using EOF was indicated by the first and second principal components (PC1 and PC2) suggesting that drought events exhibit a positive orientation to crop productivity, especially maize in Southeast Asia. The pattern of climate oscillations, such as the El Niño-Southern Oscillation (ENSO), is likely related to crop productivity in Southeast Asia. The findings emphasize the importance of considering temporal climatic patterns in agricultural decision-making, with implications for enhancing regional climate resilience and ensuring sustainable food security in the face of global climate change.

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“…Finally, the recent availability of low-cost remote sensing devices has spurred research efforts into exploring the usage of real-time, digitally transmitted information in supporting plantation management. Applications can be found relative to fertilizer scheduling (Colaço et al, 2021), poultry farm monitoring (Bumanis et al, 2022), plantation yield prediction (Kouadio et al, 2021), machinery cost estimation (Mattetti et al, 2022), and the usage of mobile-phone technology (Ahikiriza et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Decision-Analytic, Simulation-Based Model for Plantation Management Under Uncertainty in a Competitive Environment

Hernández¹,

Arrieta²,

Mantilla³

2023

Universal Journal of Operations and Management

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This work takes a decision-analytic approach to develop a prescriptive model for three essential plantation management decisions: the size of the cultivated area, the method of cultivation, and the price at which the produce will be sold. These decisions are key to plantation performance and are made difficult by uncertainty and the fact that they are neither made simultaneously nor at the same informational state, aspects that the presented approach explicitly includes. The model is developed by addressing the management of a hypothetical plantation. Decision trees are used to represent the structure of the problem, and simulation is employed to calculate the plantation's expected sales given the market conditions. The simulation relies on a customer preference model that fulfills Keeney's Value-Focused Thinking (VFT) requirements for a proper value representation. The results are presented as recommendations for planted areas and cultivation methods, while the implemented selling price depends on the conditions observed at harvest (market and produce characteristics). According to the results, the presented approach is successful in guiding plantation management decisions and may be useful in increasing plantation competitiveness.

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Probabilistic yield forecasting of robusta coffee at the farm scale using agroclimatic and remote sensing derived indices

Cited by 17 publications

References 44 publications

Proposed Open Systems Model and Intelligent Agriculture in Coffee Farms within the Harvesting Process

Proposed Open Systems Model and Intelligent Agriculture in Coffee Farms within the Harvesting Process

Historical Analysis of the Effects of Drought on Rice and Maize Yields in Southeast Asia

Decision-Analytic, Simulation-Based Model for Plantation Management Under Uncertainty in a Competitive Environment

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