The Classification of Cropping Patterns Based on Regional Climate Classification Using Decision Tree Approach

Abstract

Nowadays, agricultural field is experiencing problems related to climate change that result in the changing patterns in cropping season, especially for paddy and coarse grains, pulses roots and Tuber (CGPRT/Palawija) crops. The cropping patterns of rice and CGPRT crops highly depend on the availability of rainfall throughout the year. The changing and shifting of the rainy season result in the changing cropping seasons. It is important to find out the cropping patterns of paddy and CGPRT crops based on monthly rainfall pattern in every area. The Oldeman's method which is usually used in the classification of of cropping patterns of paddy and CGPRT crops is considered less able to determine the cropping patterns because it requires to see the rainfall data throughout the year. This research proposes an alternative solution to determine the cropping pattern of paddy and CGPRT crops based on the pattern of rainfall in the area using decision tree approach. There were three algorithms, namely, J48, RandomTree and REPTree, tested to determine the best algorithm used in the process of the classification of the cropping pattern in the area. The results showed that J48 algorithm has a higher classification accuracy than RandomTree and REPTree for 48%, 42.67% and 38.67%, respectively. Meanwhile, the results of data testing into the decision tree rule indicate that most of the areas in DKI Jakarta are suggested to apply the cropping pattern of 1 paddy cropping and 1 CGRPT cropping (1 PS + 1 PL). While in Banten, there are three cropping patterns that can be applied, they are; 1 paddy cropping and 1 CGPRT cropping (1 PS + 1 PL), 3 short-period paddy croppings or 2 paddy croppings and 1 CGPRT cropping (3 short-period PS or 2 PS + 1 PL) and 2 paddy croppings and 1 CGPRT cropping (2 PS + 1 PL).

Monitoring Watering Practices on European Rice Paddy Fields with Sentinel-1: Characterizing European Rice in Multitemporal SAR Sequences for Mapping and Traceability Purposes

“…Polarimetric decomposition [32,[48][49][50] Decision trees [40][41][42][43][44] Analysis of histograms [45][46][47] 4. Study area and data 4.1.…”

“…In Table 1 the reader may find summarized the most common radar-based state-ofthe-art techniques involved in rice mapping and monitoring applications together with the corresponding literature references. A common method to map rice fields takes advantage of decision trees and/or random forest classifiers with different input features [40][41][42][43][44], relying only on SAR backscatter time series. In this type of methodology, classification is performed by a simple phenology-based decision tree.…”

Mapping European Rice Paddy Fields Using Yearly Sequences of Spaceborne Radar Reflectivity: A Case Study in Italy

“…A common method to map rice fields takes advantage of decision trees and/or random forest classifiers with different input features [41][42][43][44][45], relying only on SAR backscatter time-series. In this type of methodology, classification is performed by a simple phenology-based decision tree.…”

Predicting future dry season periods for irrigation management in West Sumatra, Indonesia

“…Changes in the local climate affect rainfall patterns and spatial distribution in a year, which forces farmers to shift the period of rice cultivation in the dry season. Daily rainfall is an important climatic variable in determining the cropping pattern for rice and other crops (Munandar and Sumiati 2017;Nugroho and Nuraini 2016) and the planting schedule (Sofia et al 2019;Caraka et al 2018) and in identifying the dry season period (Irsyad et al 2014;Omotosho 1992). According to the report of the Intergovernmental Panel on Climate Change (IPCC) (2013), the global climate has changed and has an impact on rainfall patterns in some countries, including Indonesia.…”

“…Various studies have discussed the onset and end of the dry season, and even those of the rainy season for agricultural management (Laux et al 2008;Li and Fu 2006;Marengo et al 2001;Omotosho 1992;Caraka et al 2018). Thus, the dry season parameters (onset, end, and duration) are essential for irrigation management in rice cultivation (Munandar and Sumiati 2017;Oue and Laban 2020). Irsyad et al (2014) presented accumulated daily rainfall in a year as the cumulative rainfall and were able to define the dry season period in a year with high precision by using a 5thdegree polynomial equation (R 2 > 0.98).…”