Effects of climate change on dryland agriculture vegetation index in Nangapanda, East Nusa Tenggara

Abstract

Dryland agriculture produces agricultural commodities in the food and plantation sectors. However, the potential for dryland agriculture in Indonesia is one of the agricultural bases, which is also threatened by climate anomalies. This research aims to examine one of the climatic factors, namely Land Surface Temperature (LST), which is influenced by environmental carrying capacity factors, namely the vegetation index on the productivity of dryland agriculture. The vegetation indexes used are NDVI, SAVI, and EVI, using Landsat 5 TM and Landsat 8 OLI imagery for 1999, 2004, 2009, 2014, and 2019 then analyzed by statistical regression tests. Another data used are temperature comparison data from the Meteorological Climatological and Geophysical Agency of Indonesia as known as Badan Meteorologi Klimatologi dan Geofisika (BMKG), agricultural productivity data from Statistics Indonesia as known as Badan Pusat Statistik (BPS), and Agricultural Counseling Agency of Nangapanda District as known as Badan Penyuluhan Pertanian (BPP). The result obtained from this research is that there is a significant inverse relationship between the vegetation index and LST. Later, the increase in LST can cause a decrease in the productivity of dryland agriculture.

Carbon sink and greenhouse gas emission of dryland vegetation cover in tourism villages in Flores Island, East Nusa Tenggara, Indonesia

“…In Indonesia, dryland ecosystems are characterized by savanna, high temperature, and less precipitation in East Nusa Tenggara Province (Buditama et al 2021;Sutomo and van Etten 2021). Generally, despite the growing numbers and potential of village tourism in East Nusa Tenggara, there is still a scarcity of data on the carbon stock and GHG emissions in some villages designated as tourist destinations.…”

Vegetation response to climate and climatic extremes in northwest Bangladesh: a quantile regression approach

“…Generally, different kinds of correlation and regression analyses are used to assess the nexus between climate and vegetation. In literature, the association of vegetation with climatic variables has been estimated different methods, including anomaly analysis (Zhao et al, 2018), Pearson correlation analysis (Buditama et al, 2021), partial correlation analysis (Yan et al, 2021), Time lag cross-correlation method (Wang et al, 2021), geographical detector method (Zhao et al, 2021), residual trend analysis (Ge et al, 2021), Recurrence Plots (Almeida-Ñauñay et al, 2021, Spearman rank correlation (Alhumaima and Abdullaev, 2020),Multiple wavelet coherence (Cheng et al, 2021), coe cient of variation model (Sun et al, 2021),and principal components regression (You et al, 2021). The impact of climate variables on vegetation has been quanti ed using varieties of regression methods, including simple linear regression (Li et al, 2021) and least absolute shrinkage and selection operator logistic regression (Wang et al, 2021).…”

Vegetation Response to Climate and Climatic Extremes in Northwest Bangladesh: A Quantile Regression Approach

“…Generally, different kinds of correlation and regression analyses are used to assess the nexus between climate and vegetation. In literature, the association of vegetation with climatic variables has been estimated different methods, including anomaly analysis (Zhao et al, 2018), Pearson correlation analysis (Buditama et al, 2021), partial correlation analysis (Yan et al, 2021), Time lag cross-correlation method (Wang et al, 2021), geographical detector method (Zhao et al, 2021), residual trend analysis (Ge et al, 2021), Recurrence Plots (Almeida-Ñauñay et al, 2021), Spearman rank correlation (Alhumaima and Abdullaev, 2020),Multiple wavelet coherence (Cheng et al, 2021), coe cient of variation model (Sun et al, 2021),and principal components regression (You et al, 2021). The impact of climate variables on vegetation has been quanti ed using varieties of regression methods, including simple linear regression (Li et al, 2021) and least absolute shrinkage and selection operator logistic regression (Wang et al, 2021).…”