Crop yield outlooks in the Iberian Peninsula: Connecting seasonal climate forecasts with crop simulation models

Capa-Morocho, Mirian; Ines, Amor Valeriano M.; Baethgen, Walter E.; Rodríguez‐Fonseca, Belén; Han, Eunjin; Ruiz‐Ramos, Margarita

doi:10.1016/j.agsy.2016.08.008

Cited by 44 publications

(19 citation statements)

References 46 publications

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“…2 line 6. The significant negative trends are related to the period 1901-2012 in Páscoa et al (2017a) and to 1901-2000in Sousa et al (2011. In order to be clearer, we propose rephrasing to: "Recent works have found significant negative trends of drought indexes in the IP based on long-term time-series including the entire 20th century, particularly in southern regions (Páscoa et al, 2017a;Sousa et al, 2011), and the expected declining of crop yields due to future warming conditions is being pointed out (Ferrise et al, 2011; Hernández-Barrera and Rodríguez-Puebla, 2017).…”

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confidence: 90%

“…We propose rephrasing to the following in a revised version: "On the other hand, dynamical crop models describing the biological processes are one of the existing tools used to assess crop productivity, e.g. CERES (Crop Environment REsource Synthesis) models (Capa-Morocho et al, 2016;Hlavinka et al, 2010) and AquaCrop (Paredes et al, 2016;Vergni et al, 2015). These crop models are important tools in agrometeorological studies being able to compute irrigation requirements and yield simulations, and have been particularly useful for assessing the impacts of climate change on agricultural productions (Hlavinka et al, 2010).…”

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confidence: 99%

“…CERES (Crop Environment REsource Synthesis) models (Capa-Morocho et al, 2016;Hlavinka et al, 2010) and AquaCrop (Paredes et al, 2016;Vergni et al, 2015). These crop models are important tools in agrometeorological studies being able to compute irrigation requirements and yield simulations, and have been particularly useful for assessing the impacts of climate change on agricultural productions (Hlavinka et al, 2010). However, such models are limited in their ability to quantify the impact of climate variability on crop yields over larger scales (Estes et al, 2013) and the detailed representation of crop's biophysical interactions requires demanding parameterization settings and in-C3 put data (Paredes et al 2014;Giménez et al 2016;Paredes et al 2016).…”

mentioning

confidence: 99%

“…These crop models are important tools in agrometeorological studies being able to compute irrigation requirements and yield simulations, and have been particularly useful for assessing the impacts of climate change on agricultural productions (Hlavinka et al, 2010). However, such models are limited in their ability to quantify the impact of climate variability on crop yields over larger scales (Estes et al, 2013) and the detailed representation of crop's biophysical interactions requires demanding parameterization settings and in-C3 put data (Paredes et al 2014;Giménez et al 2016;Paredes et al 2016). Thus, empirical modelling constitutes an alternative to represent the large-scale impacts of drought conditions in the agricultural sector (Vicente-Serrano et al 2006;Matsumura et al 2015;Kogan et al 2015a) requiring lower computation costs than mechanistic modelling (Ferrise et al 2011;Estes et al 2013).…”

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Ribeiro¹

2019

Preprint

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Ribeiro¹

2019

Preprint

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“…Ines, W.E. Baethgen, B. Rodríguez-Fonseca, E. Han, and M. Ruiz-Ramos, [13] presented an outlook model for crop yield maintenance in the region of e Iberian Peninsula. The proposed model helps to improve the security of food and economical demand of large farming.…”

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Weather and Crop Prediction Using Modified Self Organizing Map for Mysore Region

Mohan¹,

Patil²

2018

IJIES

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Abstract:In India Rice and ragi are the two pre-eminent crops that stable's the food of eastern and southern part of the country. These two crops mainly grown in rain fed areas, which receives heavy annual rainfall. In this scenario, Mysore district is undertaken for experiment analysis. As the region receives heavy rainfall (782mm). In this region the rainfall rate is high but the productivity of this crops are very low due to irrelevant crop cultivation. In order to overcome this concern, an advanced technology named Weighted-Self Organizing Map (W-SOM) is employed for accurate crop and weather prediction, which is the combination of both Self Organizing Map (SOM) and Learning Vector Quantization (LVQ). In this paper, the prediction accuracy is enhanced by minimizing the Within Class Error (WCE) among the clusters. Therefore, this new approach outcome shows a clear idea about suitable crop cultivation in Mysore. Experimental outcome shows that the proposed approach improved accuracy in crop and weather prediction up to 0.5-2% compared to the existing methods: SOM, Kernel-Nearest Neighbors (KNN) and Ensemble Neural Network (ENN).

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The role of crop simulation modeling in assessing potential climate change impacts

Timlin,

Paff,

Han

2024

Agrosystems Geosci & Env

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Agriculture is weather dependent, and changes in climate can have a drastic impact on our ability to feed, fuel, and clothe the world's population. Climate change is causing more frequent and unprecedented extreme weather events that are already negatively affecting agriculture. We need to assess the effects of extreme temperatures and rainfall on agriculture. Patterns of short‐term extreme weather events, such as elevated temperatures, flooding, and strong winds, are not predictable enough to design field experiments around. Process‐based crop and soil simulation models allow us to explore new management options and thus provide whole‐system‐based knowledge and management guides for different locations over variable climate conditions. By using crop simulation models, researchers can test different adaptation strategies and assess their effectiveness in reducing the impacts of climate change on agricultural production. In this paper, we discuss the development of crop models and how they have been used to assess the effects of a changing climate on agricultural productivity and propose methods for agriculture to adapt to those changes. We describe potential applications of crop models to assess regional issues such as irrigation demand, greenhouse gas emissions, and policy decisions. Better understanding of how weather and climate forecasts at various scales are provided and the reliability of these forecasts is important for using crop models as a planning tool. Different approaches for simulating long‐term climate change impacts on crop yield and seasonal yield forecasting are discussed. The use of ensemble models to better assess climate change impacts is also discussed.

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Crop yield outlooks in the Iberian Peninsula: Connecting seasonal climate forecasts with crop simulation models

Cited by 44 publications

References 46 publications

Authors reply to Anonymous Referee#2

Authors reply to Anonymous Referee#2

Weather and Crop Prediction Using Modified Self Organizing Map for Mysore Region

The role of crop simulation modeling in assessing potential climate change impacts

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