Prediction of Bhutan's ecological distribution of rice (<i>Oryza sativa</i>L.) under the impact of climate change through maximum entropy modelling

Chhogyel, Ngawang; Kumar, Lalit; Bajgai, Yadunath; Jayasinghe, Layomi Sadeeka

doi:10.1017/s0021859620000350

Cited by 39 publications

(23 citation statements)

References 72 publications

(109 reference statements)

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“…The used Maxent to produce maps of land suitability for each crop. We chose Maxent, as it is the most frequently used ML algorithm for mapping land suitability [40][41][42][43][44][45][46][47][48][49]53,[78][79][80], although a few studies have applied other algorithms [49,50,52,80]. Researchers in ecology originally developed Maxent as a species distribution model, using environmental variables as inputs [81].…”

Section: Models and Predictionsmentioning

confidence: 99%

“…The risk of misinterpretation is especially relevant when researchers aim to map temporal shifts in land suitability. Studies aiming to map climate-induced changes in land suitability using ML have generally focused on growing conditions [41][42][43][44][45][46]52], implicitly aiming to map ecological suitability, omitting socioeconomic variables. Therefore, they could not identify important socioeconomic variables, their effects, and their interactions.…”

Section: Ecological and Socioeconomic Suitabilitymentioning

confidence: 99%

“…ML has gained widespread use in soil mapping [35,36], species distribution modeling [37,38], land use mapping, and land cover classification [39]. The most common ML approach to land suitability assessment relies on models trained with the Maxent algorithm using all the available land use data with no socioeconomic covariates [40][41][42][43][44][45][46]. This approach builds on the assumption that farmers cultivate crops in the areas where they have the best growing conditions [47].…”

Section: Introductionmentioning

confidence: 99%

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Can We Use Machine Learning for Agricultural Land Suitability Assessment?

et al. 2021

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It is vital for farmers to know if their land is suitable for the crops that they plan to grow. An increasing number of studies have used machine learning models based on land use data as an efficient means for mapping land suitability. This approach relies on the assumption that farmers grow their crops in the best-suited areas, but no studies have systematically tested this assumption. We aimed to test the assumption for specialty crops in Denmark. First, we mapped suitability for 41 specialty crops using machine learning. Then, we compared the predicted land suitabilities with the mechanistic model ECOCROP (Ecological Crop Requirements). The results showed that there was little agreement between the suitabilities based on machine learning and ECOCROP. Therefore, we argue that the methods represent different phenomena, which we label as socioeconomic suitability and ecological suitability, respectively. In most cases, machine learning predicts socioeconomic suitability, but the ambiguity of the term land suitability can lead to misinterpretation. Therefore, we highlight the need for increasing awareness of this distinction as a way forward for agricultural land suitability assessment.

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Section: Models and Predictionsmentioning

confidence: 99%

Section: Ecological and Socioeconomic Suitabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Can We Use Machine Learning for Agricultural Land Suitability Assessment?

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“…For modeling purposes, the set of bioclimatic variables available on the WorldClim website has been widely used [23,36,37]. Generally, the 'current' climate data sourced from the WorldClim use the years 1950-2000 to calculate climatic averages.…”

Section: Bioclimatic Variablesmentioning

confidence: 99%

Climate Change May Imperil Tea Production in the Four Major Tea Producers According to Climate Prediction Models

Jayasinghe

Kumar

2020

Agronomy

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The threat of accelerating climate change on species distribution now and in the future is a topic of increasing research interest. However, little work has been undertaken to assess how shifting climates will affect the suitability of tea cultivation. Therefore, we used MaxEnt modelling to project the impact of current and future climatic scenarios on the potential distribution of tea across the four tea-producing countries of China, India, Kenya and Sri Lanka. Projections were made for the years 2050 and 2070 with three Representative Concentration Pathways (RCPs) using seven bioclimatic predictors under three global circulation models (GCMs). The current and future habitat suitability for tea predicted by the models produced a high accuracy rate, with high areas under the receiver operating characteristic curve (AUCs) for all tested RCPs under the three GCMs for the four countries. The mean true skill statistic (TSS) values for tea in Sri Lanka, Kenya, India and China were 0.80, 0.91, 0.91, and 0.74, respectively. The kappa values (k) of the current and future models for all four countries ranged from 0.40 to 0.75, which indicates that the overall performance of the model was good. The precipitation seasonality and annual precipitation were found to be the most influential variables in Sri Lanka and India, respectively, while annual mean temperature was the most effective contributor for determining the suitability of habitat for tea in Kenya and China. An important proviso is that some existing tea-growing areas will face reduced suitability for future tea cultivation suggesting that by 2050 there will be a drastic reduction in the optimal suitability by averages of 26.2%, 14%, and 4.7% in Kenya, Sri Lanka and China, respectively. The optimal suitability will be reduced by 15.1%, 28.6% and 2.6% in Kenya, Sri Lanka and China, respectively, by 2070. India displays an advantage in projected future climates as it gains optimal suitability areas of 15% by 2050 and 25% by 2070.

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“…For instance, different climate change scenarios predicted that wheat and maize production areas in Pakistan would decline by 30-35 and 23-36%, respectively, by 2070 (Khubaib et al, 2021). Similarly, in Bhutan, it is projected that rice production areas in all major rice-growing areas of Paro, Wangdue, Punakha, Tsirang, Dagana, Trashigang, Trashi Yangtse, and Samtse are likely to shrink in the projected future climate (Chhogyel et al, 2020). However, more systematic research is necessary to better understand the impacts of climate change on crop suitability and crop area.…”

Section: Effects Of Climate Change On Crop Yields In South Asiamentioning

confidence: 99%

A Framework for Addressing the Twin Challenges of COVID-19 and Climate Change for Sustainable Agriculture and Food Security in South Asia

Rasul

2021

Front. Sustain. Food Syst.

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Climate change has begun to ravage agriculture and threaten food security in many parts of the world. The novel coronavirus pandemic (COVID-19) has further disrupted agricultural activities and supply chains and has become a serious threat for public health. Like in many developing countries, South Asian farmers are now facing the double challenge of addressing the impacts of a changing climate and managing the disruptions caused by COVID-19. Despite growing concern, there is limited understanding of how climate change, public health, and COVID-19 interact, and of the possible pathways to achieving a climate-friendly recovery from COVID-19 to achieve food and nutrition security. In view of this, this paper explores the multifaceted challenges that farmers are now facing in South Asia due to climate change and the disruption caused by COVID-19 from the agricultural and food security lens. The analysis reveals that the complex interactions of COVID-19 and climate change have impacted all dimensions of food security. These interlinkages demand an integrated approach in dealing with food, public health, and climate change to harness synergies and minimize trade-offs between food production, public health, and climate mitigation. I present a framework to address the immediate challenge of COVID-19 and the longer-term challenge of anthropogenic climate change. Key elements of the framework include the strengthening health sector response capacities, strengthening of local and regional food systems, making agriculture resilient to pandemics, adopting flexible and smart approaches—including the implementation of climate-smart agricultural interventions on different scales, promotion of appropriate research and innovation, and the integration of short-term support to address the challenges of COVID-19 to build long-term productivity, and resilience of food systems by investing on natural capital. This framework would enable policy makers to choose the appropriate policy responses at different scales, to address these twin challenges of COVID-19 and climate change.

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Prediction of Bhutan's ecological distribution of rice (Oryza sativaL.) under the impact of climate change through maximum entropy modelling

Cited by 39 publications

References 72 publications

Can We Use Machine Learning for Agricultural Land Suitability Assessment?

Can We Use Machine Learning for Agricultural Land Suitability Assessment?

Climate Change May Imperil Tea Production in the Four Major Tea Producers According to Climate Prediction Models

A Framework for Addressing the Twin Challenges of COVID-19 and Climate Change for Sustainable Agriculture and Food Security in South Asia

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