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Agroforestry Suitability for Planning Site-Specific Interventions Using Machine Learning Approaches. SUSTAINABILITY 2022. [DOI: 10.3390/su14095189] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Agroforestry in the form of intercropping, boundary plantation, and home garden are parts of traditional land management systems in India. Systematic implementation of agroforestry may help achieve various ecosystem benefits, such as reducing soil erosion, maintaining biodiversity and microclimates, mitigating climate change, and providing food fodder and livelihood. The current study collected ground data for agroforestry patches in the Belpada block, Bolangir district, Odisha state, India. The agroforestry site-suitability analysis employed 15 variables on climate, soil, topography, and proximity, wherein the land use land cover (LULC) map was referred to prescribe the appropriate interventions. The random forest (RF) machine learning model was applied to estimate the relative weight of the determinant variables. The results indicated high accuracy (average suitability >0.87 as indicated by the validation data) and highlighted the dominant influence of the socioeconomic variables compared to soil and climate variables. The results show that >90% of the agricultural land in the study area is suitable for various agroforestry interventions, such as bund plantation and intercropping, based on the cropping intensity. The settlement and wastelands were found to be ideal for home gardens and bamboo block plantations, respectively. The spatially explicit data on agroforestry suitability may provide a baseline map and help the managers and planners. Moreover, the adopted approach can be hosted in cloud-based platforms and applied in the different agro-ecological zones of India, employing the local ground data on various agroforestry interventions. The regional and national scale agroforestry suitability and appropriate interventions map would help the agriculture managers to implement and develop policies.
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Mugiyo H, Chimonyo VG, Sibanda M, Kunz R, Masemola CR, Modi AT, Mabhaudhi T. Evaluation of Land Suitability Methods with Reference to Neglected and Underutilised Crop Species: A Scoping Review. LAND 2021; 10:125. [PMID: 39036712 PMCID: PMC7616268 DOI: 10.3390/land10020125] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In agriculture, land use and land classification address questions such as "where", "why" and "when" a particular crop is grown within a particular agroecology. To date, there are several land suitability analysis (LSA) methods, but there is no consensus on the best method for crop suitability analysis. We conducted a scoping review to evaluate methodological strategies for LSA. Secondary to this, we assessed which of these would be suitable for neglected and underutilised crop species (NUS). The review classified LSA methods reported in articles as traditional (26.6%) and modern (63.4%). Modern approaches, including multi-criteria decision-making (MCDM) methods such as analytical hierarchy process (AHP) (14.9%) and fuzzy methods (12.9%); crop simulation models (9.9%) and machine learning related methods (25.7%) are gaining popularity over traditional methods. The MCDM methods, namely AHP and fuzzy, are commonly applied to LSA while crop models and machine learning related methods are gaining popularity. A total of 67 parameters from climatic, hydrology, soil, socio-economic and landscape properties are essential in LSA. Unavailability and the inclusion of categorical datasets from social sources is a challenge. Using big data and Internet of Things (IoT) improves the accuracy and reliability of LSA methods. The review expects to provide researchers and decision-makers with the most robust methods and standard parameters required in developing LSA for NUS. Qualitative and quantitative approaches must be integrated into unique hybrid land evaluation systems to improve LSA.
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Affiliation(s)
- Hillary Mugiyo
- Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth & Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Pietermaritzburg3209, South Africa
| | - Vimbayi G.P. Chimonyo
- Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth & Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Pietermaritzburg3209, South Africa
| | - Mbulisi Sibanda
- Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth & Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Pietermaritzburg3209, South Africa
- Department of Geography, Environmental Studies and Tourism, University of the Western Cape, Private Bag X17, Bellville7535, South Africa
| | - Richard Kunz
- Centre for Water Resources Research, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Pietermaritzburg3209, South Africa
| | - Cecilia R. Masemola
- Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth & Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Pietermaritzburg3209, South Africa
| | - Albert T. Modi
- Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth & Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Pietermaritzburg3209, South Africa
| | - Tafadzwanashe Mabhaudhi
- Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth & Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Pietermaritzburg3209, South Africa
- Centre for Water Resources Research, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, P/Bag X01, Pietermaritzburg3209, South Africa
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Wong YJ, Arumugasamy SK, Chung CH, Selvarajoo A, Sethu V. Comparative study of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and multiple linear regression (MLR) for modeling of Cu (II) adsorption from aqueous solution using biochar derived from rambutan (Nephelium lappaceum) peel. ENVIRONMENTAL MONITORING AND ASSESSMENT 2020; 192:439. [PMID: 32556670 DOI: 10.1007/s10661-020-08268-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 03/31/2020] [Indexed: 05/22/2023]
Abstract
Presence of copper within water bodies deteriorates human health and degrades natural environment. This heavy metal in water is treated using a promising biochar derived from rambutan (Nephelium lappaceum) peel through slow pyrolysis. This research compares the efficacies of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models and evaluates their capability in estimating the adsorption efficiency of biochar for the removal of Cu (II) ions based on 480 experimental sets obtained in a laboratory batch study. The effects of operational parameters such as contact time, operating temperature, biochar dosage, and initial Cu (II) ion concentration on removing Cu (II) ions were investigated. Eleven different training algorithms in ANN and 8 different membership functions in ANFIS were compared statistically and evaluated in terms of estimation errors, which are root mean squared error (RMSE), mean absolute error (MAE), and accuracy. The effects of number of hidden neuron in ANN model and fuzzy set combination in ANFIS were studied. In this study, ANFIS model with Gaussian membership function and fuzzy set combination of [4 5 2 3] was found to be the best method, with accuracy of 90.24% and 87.06% for training and testing dataset, respectively. Contribution of this study is that ANN, ANFIS, and MLR modeling techniques were used for the first time to study the adsorption of Cu (II) ions from aqueous solutions using rambutan peel biochar.
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Affiliation(s)
- Yong Jie Wong
- Research Center for Environmental Quality Management, Graduate School of Engineering, Kyoto University, 1-2 Yumihama, Otsu, Shiga, 520-0811, Japan
| | - Senthil Kumar Arumugasamy
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500, Semenyih, Selangor, Malaysia.
| | - Chang Han Chung
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500, Semenyih, Selangor, Malaysia
| | - Anurita Selvarajoo
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500, Semenyih, Selangor, Malaysia
| | - Vasanthi Sethu
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, 43500, Semenyih, Selangor, Malaysia
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Zabel F, Putzenlechner B, Mauser W. Global agricultural land resources--a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions. PLoS One 2014; 9:e107522. [PMID: 25229634 PMCID: PMC4167994 DOI: 10.1371/journal.pone.0107522] [Citation(s) in RCA: 93] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2014] [Accepted: 08/19/2014] [Indexed: 11/18/2022] Open
Abstract
Changing natural conditions determine the land's suitability for agriculture. The growing demand for food, feed, fiber and bioenergy increases pressure on land and causes trade-offs between different uses of land and ecosystem services. Accordingly, an inventory is required on the changing potentially suitable areas for agriculture under changing climate conditions. We applied a fuzzy logic approach to compute global agricultural suitability to grow the 16 most important food and energy crops according to the climatic, soil and topographic conditions at a spatial resolution of 30 arc seconds. We present our results for current climate conditions (1981–2010), considering today's irrigated areas and separately investigate the suitability of densely forested as well as protected areas, in order to investigate their potentials for agriculture. The impact of climate change under SRES A1B conditions, as simulated by the global climate model ECHAM5, on agricultural suitability is shown by comparing the time-period 2071–2100 with 1981–2010. Our results show that climate change will expand suitable cropland by additionally 5.6 million km2, particularly in the Northern high latitudes (mainly in Canada, China and Russia). Most sensitive regions with decreasing suitability are found in the Global South, mainly in tropical regions, where also the suitability for multiple cropping decreases.
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Affiliation(s)
- Florian Zabel
- Department of Geography, Ludwig Maximilians University, Munich, Germany
- * E-mail:
| | | | - Wolfram Mauser
- Department of Geography, Ludwig Maximilians University, Munich, Germany
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Dong J, Liu J, Yan H, Tao F, Kuang W. Spatio-temporal pattern and rationality of land reclamation and cropland abandonment in mid-eastern Inner Mongolia of China in 1990-2005. ENVIRONMENTAL MONITORING AND ASSESSMENT 2011; 179:137-153. [PMID: 20949315 DOI: 10.1007/s10661-010-1724-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2010] [Accepted: 09/20/2010] [Indexed: 05/30/2023]
Abstract
The Mid-eastern Inner Mongolia of China, a typical agro-pastoral transitional zone, has undergone rapid agricultural land use changes including land reclamation and cropland abandonment in past decades due to growing population and food demand, climatic variability, and land use policy such as the "Grain for Green" Project (GFG Project). It is significant to the regional ecology and sustainability to examine the pattern and its rationality of land use change. The processes of land reclamation and cropland abandonment were accessed by using land use change dataset for four periods of 1990, 1995, 2000, and 2005, derived from the interpretation of Landsat TM images. And then the rationality of land reclamation and cropland abandonment was analyzed based on the habitat suitability for cultivation. The results indicated that: (1) land reclamation was the dominant form of agricultural land use change from 1990 to 2005, the total cropland area increased from 64,954.64 km(2) in 1990 to 76,258.51 km(2) in 2005; However, the speed of land reclamation decreased while cropland abandonment increased around 2000. The Land Reclamation Degree decreased from 1995-2000 to 2000-2005, meanwhile, Cropland Abandonment Degree increased. (2) As for the habitat suitability levels, moderately and marginally suitable levels had largest areas where cropland was widespread. Pattern of agricultural land use trended to become more rational due to the decrease of land reclamation area in low suitable levels and the increase of cropland abandonment in unsuitable area after 2000. (3) The habitat suitability-based rationality analysis of agricultural land use implicated that the GFG Project should take cultivation habitat suitability assessment into account.
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Affiliation(s)
- Jinwei Dong
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.
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