Decoupling pollution-agricultural growth and predicting climate change impacts on decoupling index using Bayesian network in different climatic regions

Naghavi, Somayeh; Ebrahimi-Khusfi, Zohre; Mirzaei, Abbas

doi:10.1007/s11356-021-16662-9

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…Soil delivers a wide variety of ecosystem services (Hannam & Boer, 2004) through soil functions which are linked to the physicochemical and biological properties of soil (Adhikari, K., & Hartemink, A. E., 2016). Overuse of chemical fertilizers in agricultural land is a main source of carbon emission and also directly threatens human, soil and aquatic health (Naghavi et al, 2022). Characterizing the soil through fine-resolution surveying and use of classification models for correctly grouping the land are important steps for identifying the appropriate soil management practices in precision agriculture (Reddy et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Overuse of chemical fertilizers in agricultural land is a main source of carbon emission and also directly threatens human, soil and aquatic health (Naghavi et al, 2022). Characterizing the soil through fine-resolution surveying and use of classification models for correctly grouping the land are important steps for identifying the appropriate soil management practices in precision agriculture (Reddy et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Characterization and classification of fine-resolution soil profile for precision agriculture using random forest and self-organizing map

Elias,

Sharma,

Goel

2024

Preprint

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The availability of high throughput soil profile information is an important component in precision agriculture to perform efficient soil management for sustainable production. We collected 14 soil physiochemical features from Nagpur, Pune, and Haveri, representing target environments of safflower cultivation, and also from our experiment station at Delhi, at fine resolution and created graphical maps to depict the variability. Additionally, we evaluated the predictive ability of two statistical learning models, random forest (RF) and self-organizing maps (SOM) against multinomial regression models for correctly classifying the soil profile. Clustering was performed around the medoids produced from the dissimilarity matrices of these models using partitioning around medoids (PAM) model. The robustness, versatility, and predictive ability of models in correctly classifying the soil profile to clusters were then tested using cross-validation which was repeated 100 times. This study was performed using training data with proportionate size varying from 60 to 95%, and increasing the unit area of observation up to nine times (or decreasing the total number of observations up to a ninth). RF model was found to be the best performing with average prediction accuracy above 85% in all settings which reached close to 100% in some settings. The predictive ability of all the models was maintained even when only the most influencing six variables were used for classification. The optimal training population size for prediction was found to be 70 to 80%. Based on our study, it is recommended to i) collect fine resolution edaphic features from a marginal farm before crop season, ii) use RF or SOM model to identify the most influencing features distinguishing the soil samples iii) expand the area of sample collection, find values for the most influencing features, and use RF model to correctly predict the class to which the new set of the soil belongs to.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterization and classification of fine-resolution soil profile for precision agriculture using random forest and self-organizing map

Elias,

Sharma,

Goel

2024

Preprint

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“…Therefore, it is necessary to use an effective and comprehensive index to consider the environmental effects of expanding the use of chemical inputs (Xu et al, 2022). Based on this, by introducing the decoupling index (DEC), researchers considered separating pollution from the growth of agricultural production (Xiong et al, 2016;Li et al, 2019b;Chen et al, 2020, Naghavi et al, 2022, Mardani Najafabadi et al, 2022b. The DEC index discusses the harmonious development of both economic and environmental dimensions and separates agricultural economic growth from pollution (Shi, 2020).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of water-energy-food-environment-agricultural economic growth nexus integrated approach to achieve sustainable production

Naghavi

Mirzaei²,

Sardoei

et al. 2023

Environ Sci Pollut Res

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Today, growing the production of the agricultural sector in order to meet the increasing demand of food is happening through excessive consumption of water resources, energy, chemical fertilizers and pesticides, especially in developing countries. Therefore, the present study aims to design a new approach for sustainable management of resources and realization of clean production in the agricultural sector. In order to design the new approach of the present study, the integration of two concepts of water-energyfood (WEF) nexus and decoupling pollution-agricultural growth (DEC) was used. The proposed approach is modeled through the multi-objective programming model by optimizing the WEF and DEC indices, which is the rst research effort in this eld. In this regard, the performance of this approach was evaluated in Jiroft county in Kerman province, Iran. The results of the study showed that the consumption of chemical fertilizers and pesticides in the optimal pattern with the simultaneous provision of WEF and gross margin (GM) goals (Optimal 1) is about 110 kg and 0.19 liters per hectare, respectively, more than the proposed pattern (Optimal 3). On the other hand, the consumption of water and energy resources in the optimal pattern with the simultaneous provision of DEC and GM goals (Optimal 2) is about 8785 cubic meters and 1108 MJ per hectare more than the proposed pattern (Optimal 3), respectively. Therefore, this approach, due to the simultaneous integration of two concepts of sustainable development in the agricultural sector, can overcome the weakness of the WEF nexus in not paying attention to the issues of environmental pollution affected by the use of chemical inputs and the weakness of separating pollution-agricultural growth in not paying attention to the management of water and energy resources in the agricultural sector.

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“…Environmental pollution emerged as a critical global challenge in recent decades, having profound effects on various aspects of human life. Among the sectors most vulnerable to environmental pollution, agriculture stands at the forefront due to its heavy reliance on natural resources (Çetin et al, 2020;Harizanova-Bartos & Stoyanova, 2018;Kwakwa et al, 2022;Naghavi et al, 2022). This research article aims to explore the effects of environmental pollution on agricultural production as a proxy for food security, shedding light on the consequences and underlying mechanisms that drive this interaction.…”

Section: Introductionmentioning

confidence: 99%

The effects of environmental pollution on food security in Malaysia

2023

SMRJ

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Environmental pollution has emerged as a critical global challenge, significantly affecting various aspects of human life, including food security. This research paper investigates the effects of environmental pollution on agricultural output (a proxy for food security) in Malaysia from 1990 to 2020, utilising the Autoregressive Distributed Lag (ARDL) method. The results obtained from the bounds cointegration testing approach provided strong evidence supporting the existence of a long-term relationship among Methane emissions in the energy sector (thousand metric tons of CO2 equivalent) (CH4), Carbon dioxide emissions (metric tons per capita) (CO2), Nitrous oxide emissions in the energy sector (thousand metric tons of CO2 equivalent) (N2O), GDP, population, index of agricultural total factor productivity, and agricultural output. Estimation of long-run and short-run ARDL with diagnostic and model stability reveals that there is a positive correlation between the index of agricultural total factor productivity (TFP) and agricultural output in Malaysia in all four of the models analysed. The discoveries illuminate the possible mechanisms by which environmental pollution affects food security in Malaysia, offering valuable insights to policymakers and stakeholders for crafting effective strategies toward sustainable development. By uncovering the implications of environmental degradation on food availability, access, and nutritional quality, the study highlights the urgency of addressing environmental challenges to ensure long-term food security for the Malaysian population. Moreover, the study can serve as a basis for formulating sustainable policies that promote resource conservation, waste management, and eco-friendly practices in the agricultural and industrial sectors, fostering a harmonious coexistence between economic development and environmental preservation.

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Decoupling pollution-agricultural growth and predicting climate change impacts on decoupling index using Bayesian network in different climatic regions

Cited by 10 publications

References 37 publications

Characterization and classification of fine-resolution soil profile for precision agriculture using random forest and self-organizing map

Characterization and classification of fine-resolution soil profile for precision agriculture using random forest and self-organizing map

Evaluation of water-energy-food-environment-agricultural economic growth nexus integrated approach to achieve sustainable production

The effects of environmental pollution on food security in Malaysia

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