Development of Spatial Model for Food Security Prediction Using Remote Sensing Data in West Java, Indonesia

Abstract

The food crisis is a problem that the world will face. The availability of growing areas that continues to decrease with the increase in food demand will result in a food crisis in the future. Good planning is needed to deal with future food crises. The absence of studies on the development of spatial models in estimating an area’s future food status has made planning for handling the food crisis suboptimal. This study aims to predict food security by integrating the availability of paddy fields with environmental factors to determine the food status in West Java Province. Food status modeling is done by integrating land cover, population, paddy fields productivity, and identifying the influence of environmental factors. The land cover prediction will be developed using the CA-Markov model. Meanwhile, to identify the influence of environmental factors, multivariable linear regression (MLR) was used with environmental factors from remote sensing observations. The data used are in the form of the NDDI (Normalized Difference Drought Index), NDVI (Normalized Difference Vegetation Index), land surface temperature (LST), soil moisture, precipitation, altitude, and slopes. The land cover prediction has an overall accuracy of up to 93%. From the food status in 2005, the flow of food energy in West Java was still able to cover the food needs and obtain an energy surplus of 6.103 Mcal. On the other hand, the prediction of the food energy flow from the food status in 2030 will not cover food needs and obtain an energy deficit of up to 13,996,292.42 Mcal. From the MLR results, seven environmental factors affect the productivity of paddy fields, with the determination coefficient reaching 50.6%. Thus, predicting the availability of paddy production will be more specific if it integrates environmental factors. With this study, it is hoped that it can be used as planning material for mitigating food crises in the future.

The spatial effects of the household's food insecurity levels in Ethiopia: by ordinal geo-additive model

“…Though several researches assessed the determinants of Ethiopian household food insecurity and supported the associated factors obtained in this study ( 31 , 34 , 80 , 81 ), only few researches consider the spatial pattern in Ethiopia ( 22 , 82 , 83 ) with better literature on developing country ( 18 , 84 – 90 ). However, none of literature labeled as food insecure, vulnerable, and secured based on cut-points corrected for feeding cultural difference and provided the determinants specific to the hot spot zones of food insecurity and vulnerability.…”

Monitoring Coastal Areas Using Ndwi From Landsat Image Data From 1985 Based on Cloud Computation Google Earth Engine and Apps

“…However, this research can serve as a reference for dynamic observations related to renewable energy potential (Bender et al, 2017;Ihsan et al, 2022Ihsan et al, , 2021, air pollution (Pavani and Rao, 2017;A.D. Sakti et al, 2023), hazard (Das et al, 2006;Virtriana et al, 2023), and food security (Gommes et al, 2016;Virtriana et al, 2022) in a particular region.…”

Analisis Sistem Agrometeorologi dalam Meningkatkan Ketahanan Pangan saat Kemarau

“…Penurunan produksi beras akibat perubahan iklim merupakan masalah yang signifikan, dan tingkat kemiskinan di beberapa daerah di Jawa Barat cukup tinggi. Para pembuat kebijakan harus mengambil kebijakan yang tepat terkait bidang-bidang yang membutuhkan perhatian terkait ketahanan pangan, dan informasi mengenai ketahanan pangan sangat penting untuk memberikan arahan dan rekomendasi bagi para pengambil keputusan (Alves et al, 2018;Estiningtyas & Dermoredjo, 2021;Ruminta et al, 2017;Sukmaya et al, 2022;Virtriana et al, 2022).…”

“…Pertanian merupakan kontributor penting bagi stabilitas ekonomi daerah dan menopang mata pencaharian sebagian besar penduduknya. Namun, kerentanan Jawa Barat terhadap kekeringan menjadi ancaman serius bagi ketahanan pangan provinsi ini (Virtriana et al, 2022). Kekeringan dapat mengganggu siklus panen, mengurangi hasil panen, dan menghambat akses terhadap sumber daya pangan yang penting, yang pada akhirnya memengaruhi kesejahteraan penduduk pedesaan dan perkotaan.…”

“…Menanggapi ancaman ini, sistem agrometeorologi telah dikembangkan untuk meningkatkan ketahanan pangan selama kekeringan. Sistem ini menggunakan data penginderaan jauh untuk memprediksi ketahanan pangan dengan mengintegrasikan ketersediaan lahan sawah dengan faktor lingkungan untuk menentukan status pangan di Provinsi Jawa Barat (Virtriana et al, 2022). Prediksi tutupan lahan tersebut memiliki akurasi keseluruhan hingga 93%, dan aliran energi pangan di Jawa Barat masih mampu mencukupi kebutuhan pangan dan memperoleh surplus energi sebesar 6,103 Mcal pada tahun 2005 (Virtriana et al, 2022).…”