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Integrated Cropping Calendar Information System (ICCIS) is an application used to convey innovative research results related to cropping patterns, varieties, fertilization, pest attacks, agricultural tools and machinery. This study aims to identify the influencing factors and communication models in the implementation of ICCIS in the North Sumatra region. Data analysis uses the Partial Least Squares Structural Equation Modelling (PLS-SEM) method. The result showed that the ICCIS Task Force Team (TGT Katam), characteristics of farmers, and characteristics of ICCIS have positive effects on the level of implementation of ICCIS by farmers, with variable significance values of 3.902, 3.851, and 8,296, respectively. In related to the level of application of ICCIS by farmers, the analysis results have a positive effect on the added value of the use of ICCIS with a significance value of 6.509. TGT Katam received ICCIS information from the central cropping calendar team and the technical implementation unit that oversees it. TGT Katam in the North Sumatra region delivered ICCIS information to farmers through socialization and demonstration plots. Interpersonal communication and the demonstration plot media encouraged farmers to implement ICCIS recommendations. Satisfy the local wisdom, compatibility, data accuracy, trialability, observability, and relative advantage are indicators that farmers can implement ICCIS.

Apriyana Y, Sarvina Y, Dewi ER, Pramudia A. 2017. Farmer adaptation strategy in paddy field affected by climate variability in monsoon regions. Asian J Agric 1: 9-16. Adaptation of agriculture cultivation to climate variability and climate anomalies both in paddy field especially in monsoon regions is one of the strategies to minimize the impact of these two phenomena to reduce the economic loss, particularly for food security in Indonesia. The objectives of this study are: (i) to identify onset and cropping pattern in irrigated land, rainfed and dry land in affected area due to climate anomalies, (ii) to collect information on carrying capacity water resources and adaptation practices applied by farmers and, (iii) to identify strategies for farmers on irrigated land, rainfed and dry land in the region affected by climate variability. The desk work analysis and field survey were conducted in Serang District, Banten Province, Subang District, West Java Province and Pati District, Central Jawa Province, Indonesia. The study was undertaken in three cluster activities i.e. (i) correlation analysis of climate anomalies and rainfall, (ii) field survey and, (iii) analysis of onset planting season, cropping pattern, water availability, the best planting time and irrigation schedule. The results showed that the farmers in affected area due to climate variability could adapt by shifting the onset of planting season. Farmers in irrigated lands changed their onset around 2-4 ten-days period to October II - December II. Furthermore, in rainfed areas, the onset around 4-6 ten-days period was shifted to November I - January III. For dry land their onset around 6-8 ten-days period was moved to November II - February I. The cropping pattern rice-rice-palawija/fallow was applied on irrigated land. Furthermore, the pattern of rice-rice/palawija/fallow-fallow was carried out in rainfed. Finally, the pattern of palawija-palawija/fallow-dormant was performed on the dry land. Adaptation programs dealing with climate variability in Serang and Pati Districts varied more than in Subang District. In Serang and Pati, during the first planting season, farmers applied irrigation roughly 20%-30% of water pump from the river and during second planting season, farmers in Pati District used water from well-pump, as well as in Serang that can reach 100% of the application.

The rice monitoring based on Sentinel-2 (SC-S2) has been developed for over nine months. It has been observed as the first and only system which generate rice growth stages maps in 10 m spatial resolution using machine learning in Indonesia. However, the SC-S2 use Support Vector Machine to separate the rice growth stages, which may have poor performances. The objective of this study is to investigate the performance of other classifiers to increase the performance of SC-S2. We used survey data from the field campaign in 2018 and synchronized with Sentinel-2 bands. The model dataset was trained using 61 machine learning algorithms to create 61 rice growth stages models. The models were applied to the Sentinel-2 image of part of Indramayu area. The accuracy, computational time and visual inspection score were collected, and the final score was calculated. The results are the highest final score is Shrinkage Discriminant Analysis, with overall accuracy 88.1% (pp

The relevance of climate extreme events, such as droughts, is well recognized as one of the impacts of climate change. The serious paddy droughts that occur during El Niño climate cycles has increased political awareness of the need to incorporate these in drought-forecasting and warning systems for effective drought management. In this study, we develop a paddy drought hazard forecasting system (PDFS) based on a web-based geographic information system (GIS). The Drought Hazard Index (DHI) was used to derive a deterministic forecast model, which is a simplified version of the probabilistic forecast model used in the previous version of this system. Based on the DHI, rice field areas in Indonesia were classified into four categories: low (1.0< DHI< 3.3), moderate (3.33 < DHI