The growth of the Indonesian population has led to an increase in the demand for rice, which the country has yet to satisfy. Indonesia needs a comprehensive strategy that integrates meaningful efforts to increase its agricultural production. This study aims to review the examined trends in rice yield in Indonesia for 70 years after Indonesia’s independence (1945–2016) followed by the identification of the application technology and factors that contribute to increasing rice yields to forecast sustainable food security scenarios up to 2030. This article reviews the results of research on rice production technology in Indonesia from 1945 to 2016, and the outlook for 2030. This paper examines the main points of the Indonesian transformation of rice technology: improvement of rice varieties, integrated crop management, innovations in agricultural machinery, and the Integrated Cropping Calendar Information System (ICCIS). We found that transformation has helped Indonesia increased its rice yields from 3 t ha⁻¹ prior to 1961 to 4.6 t ha⁻¹ in 1985, stagnated in 1990, and increased again in 2017 to 5.46 t ha⁻¹. The increase in yield was sustained by an increase in the harvested area owing to cropping index (CI) innovation. Food security and sustainable development remain the primary goals of Indonesia’s agricultural sector. The application of appropriate technologies and institutional innovations can assist Indonesia in achieving its food security. Therefore, the transformation of technological innovations will continue to be an essential driver of future agricultural growth, including greater use of crop varieties, machinery, and land/institutional reforms.

Climate change and its variability are some of the most critical threats to sustainable agriculture, with potentially severe consequences on Indonesia’s agriculture, such as changes in rainfall patterns, especially the onset of the wet season and the temporal distribution of rainfall. Most Indonesian farmers receive support from agricultural extension services, and therefore, design their agricultural calendar based on personal experience without considering global climate phenomena, such as La Niña and El Niño, which difficult to interpret on a local scale. This paper describes the Integrated Cropping Calendar Information System (ICCIS) as a mechanism for adapting to climate variability. The ICCIS contains recommendations on planting time, cropping pattern, planting area, varieties, fertilizers, agricultural machinery, potential livestock feed, and crop damage due to climate extremes for rice, maize, and soybean. To accelerate the dissemination of information, the ICCIS is presented in an integrated web-based information system. The ICCIS is disseminated to extension workers and farmers by Task Force of the Assessment Institute for Agricultural Technology (AIAT) located in each province. Based on the survey results, it is known that the ICCIS adoption rate is moderate to high. The AIAT must actively encourage and support the ICCIS Task Force team in each province. Concerning the technological recommendations, it is necessary to update the recommendations for varieties, fertilizer, and feed to be more compatible with local conditions. More accurate information and more intensive dissemination can enrich farmers’ knowledge, allowing for a better understanding of climate hazards and maintaining agricultural production.

Rice (Oryza sativa L.) is a staple food crop for more than half of the world’s population. Rice production is facing a myriad of problems, including water shortage, climate, and land-use change. Accurate maps of rice growth stages are critical for monitoring rice production and assessing its impacts on national and global food security. Rice growth stages are typically monitored by coarse-resolution satellite imagery. However, it is difficult to accurately map due to the occurrence of mixed pixels in fragmented and patchy rice fields, as well as cloud cover, particularly in tropical countries. To solve these problems, we developed an automated mapping workflow to produce near real-time multi-temporal maps of rice growth stages at a 10-m spatial resolution using multisource remote sensing data (Sentinel-2, MOD13Q1, and Sentinel-1). This study was investigated between 1 June and 29 September 2018 in two (wet and dry) areas of Java Island in Indonesia. First, we built prediction models based on Sentinel-2, and fusion of MOD13Q1/Sentinel-1 using the ground truth information. Second, we applied the prediction models on all images in area and time and separation between the non-rice planting class and rice planting class over the cropping pattern. Moreover, the model’s consistency on the multitemporal map with a 5–30-day lag was investigated. The result indicates that the Sentinel-2 based model classification gives a high overall accuracy of 90.6% and the fusion model MOD13Q1/Sentinel-1 shows 78.3%. The performance of multitemporal maps was consistent between time lags with an accuracy of 83.27–90.39% for Sentinel-2 and 84.15% for the integration of Sentinel-2/MOD13Q1/Sentinel-1. The results from this study show that it is possible to integrate multisource remote sensing for regular monitoring of rice phenology, thereby generating spatial information to support local-, national-, and regional-scale food security applications.

Regular monitoring and mapping of rice (Oryza Sativa) growth phases are essential for industry stakeholders to ensure food production is on track and to assess the impact of climate change on rice production. In Indonesia, high-cost field surveys have been widely used to monitor the rice growth phases. Alternatively, this research proposes a methodology to retrieve multi-temporal rice phenology (vegetative, reproductive, and ripening) and bare land mapping using medium resolution remote sensing imagery obtained from Landsat-8 Operational Land Imager (OLI) combined with machine learning techniques. In this study, we have used extensive ground validation information collected from 2014 to 2016 for training the models. This ground validation information was obtained from pre-installed webcams across Indonesia. Five different machine learning algorithms were used including random forest (RF), support vector machine (SVM) with three kernel functions (linear, polynomial, and radial) and artificial neural networks (ANN) to classify rice growth phases and bare land. This paper also evaluates the temporal evolution of rice phenology and bare land to check the prediction model consistency between two consecutive dates in 3 years. The results show that the nonlinear SVM algorithm gives the best model accuracy (70.5% with Kappa: 0.66) based on the test dataset and the lowest temporal changes (<11%). Spatial-temporal assessment of rice phenology and bare land from Landsat-8 indicated that the models were reliable and robust over different seasons and years. The distribution of rice phenology maps will enable Indonesian management authorities to supply fertilizer, allocate water resources, harvesting, and marketing facilities more efficiently.

Monitoring rice production is essential for securing food security against climate change threats, such as drought and flood events becoming more intense and frequent. The current practice to survey an area of rice production manually and in near real-time is expensive and involves a high workload for local statisticians. Remote sensing technology with satellite-based sensors has grown in popularity in recent decades as an alternative approach, reducing the cost and time required for spatial analysis over a wide area. However, cloud-free pixels of optical imagery are required to produce accurate outputs for agriculture applications. Thus, in this study, we propose an integration of optical (PROBA-V) and radar (Sentinel-1) imagery for temporal mapping of rice growth stages, including bare land, vegetative, reproductive, and ripening stages. We have built classification models for both sensors and combined them into 12-day periodical rice growth-stage maps from January 2017 to September 2018 at the sub-district level over Java Island, the top rice production area in Indonesia. The accuracy measurement was based on the test dataset and the predicted cross-correlated with monthly local statistics. The overall accuracy of the rice growth-stage model of PROBA-V was 83.87%, and the Sentinel-1 model was 71.74% with the Support Vector Machine classifier. The temporal maps were comparable with local statistics, with an average correlation between the vegetative area (remote sensing) and harvested area (local statistics) is 0.50, and lag time 89.5 days (n = 91). This result was similar to local statistics data, which correlate planting and the harvested area at 0.61, and the lag time as 90.4 days, respectively. Moreover, the cross-correlation between the predicted rice growth stage was also consistent with rice development in the area (r > 0.52, p < 0.01). This novel method is straightforward, easy to replicate and apply to other areas, and can be scaled up to the national and regional level to be used by stakeholders to support improved agricultural policies for sustainable rice production.

Balancing water supply demand is vital for sustaining livelihoods. Spatial mapping and calculating water yield dynamics due to land use changes over decades are needed to manage land resources and formulate ecological protection policies. This study mapped the supply, demand, and matching status of water product service using the Integrated Valuation of Ecosystem Service and Tradeoff (InVEST) biophysical models in the Citarum Watershed (CW) in 2000, 2010, and 2020. Moreover, this study used Exploratory Spatial Data Analysis (ESDA) and Geographic Information System (GIS) techniques to study the agglomeration characteristics and evolutionary trajectories of supply–demand over two decades. The results showed that between 2000–2010 and 2010–2020, the water supply decreased by 19.01 × 10⁸ m³ (18.28%) and 12.97 × 10⁸ m³ (15.27%), respectively. However, the water demand in the same period increased by 6.17 × 10⁸ m³ (23%) and 15.74 × 10⁸ m³ (47%), respectively. Over the decades, the contribution of land use land cover (LULC) changes to variations in water supply has yielded values ranging from 2.87% to 6.37%. The analysis of the water supply–demand imbalance indicated that the entire CW experienced water shortage, and the type of spatial matching for supply and demand is dominated by a high supply and high demand class (16.09% of the total area). Based on the level of water deficit calculation, the upstream and downstream areas were identified as zones that require ecological conservation, while the middle CW area requires ecological restoration or ecological improvement.