Estimation Of Rice Production Research Articles

AKURASI ESTIMASI PRODUKSI PADI DENGAN METODE NDVI BERDASARKAN SENTINEL-2 DI KABUPATEN TANAH LAUT KALIMANTAN SELATAN

Remote sensing is a technology that is very useful in every step of the planning process and has been used in developed countries and several regions in developing countries. The aim of this research is to interpret rice production in Tanah Laut Regency using the NDVI method based on Sentinel-2 and determine the accuracy of rice production estimates in Tanah Laut Regency using the NDVI method based on Sentinel-2. Research methods include preparation, measurement principles, data collection, radiometric correction, geometric correction, creating a vegetation index, determining the best vegetation index, estimating rice production, accuracy testing, image analysis, and rice production potential. The result of this research is a rice production estimation model of y = 0.341 + 4.319 NDVI. The NDVI estimation results in Tanah Laut Regency range from 1.36 t ha-1 to 3.36 t ha-1 with an average production of 2.67 t ha-1. The results of the analysis show that there is no real difference at the 95% confidence level between the results of the NDVI estimation of rice production using Sentinel-2 imagery and the results of the field survey of rice production based on a coefficient of determination (R2) of 0.749 and a standard error (SE) of 0.29 t ha-1.

Comparison of soil physical properties and soil-vegetation indices to predict rice productivity in Malang Regency of East Java

Rice has become the leading food commodity in Indonesia, with total production reached +54.60 million tons in 2019. However, the production tended to decrease by around 8% from 2018 to 2019, while the rice consumption increased by +1.53 tons. This study aims to develop a rice production estimation model using the soil-vegetation index transformation (MSAVI and SAVI) and soil physical properties, which has the advantage of being faster, cheaper, and more accurate than conventional methods. The soil physical properties were taken based on soil mapping units and analyzed with soil physical parameters. The results showed strong relationships between rice productivity - soil physical characteristics and rice productivity – MSAVI and EVI with r values of 0.97, 0.83, and 0.74, respectively. The soil physical properties have a better coefficient of determination and accuracy than soil-vegetation index. The prediction model of rice production by soil physical properties is formulated inward γ = -8.96+0.01 (Top Soil Sand) + 0.01 (Top Soil Silt) + 6.28 (Bulk Density) - 14.07 (Penetration) - 0.13 (Sub Soil Permeability). There is no difference in the productivity value between model and laboratory analysis result. These results indicate that the rice yield prediction model can be used for estimation purposes.

The Spatial Pattern of Rice Productivity Using Sentinel-2A Image in Cariu and Tanjungsari District, Bogor Regency

Rice is known as substantial main foodstuffs of Indonesian people. According to the Report from Indonesia Centrer of statistic (BPS), of- West Java Province 2018, Bogor Region is one of lower-rice productions which was shown by decreasing value approximately 26,307 ton between 2014 and 2015. The increasing efforts of rice productivity is required to estimate and observe a rice-grain supplying in the market, especially in Cariu and Tanjungsari as the primary and specific city of rice production in Bogor Region. To gain an accurate data, remote sensing method is applied. The using of sentinel-2A image processing which has 10 m of spatial resolution is recommended to see the rice planting phase based on the age of planting. The study aims to analyze the spatial studying of rice production using Sentinel-2A image processing with Normalize Difference Vegetation Index (NDVI) method for to determine the age of rice plants starting from the beginning of planting to the end of the harvest and also help estimate the productivity of rice paddies. The results show that rice production estimation which is planted in the altitude of under 100 masl (mean sea level) have value 5,52 ton/ha, to the optimum altitude on the range of 100-500 masl the productivity increase become 6,31 ton/ha, and back down on the altitude more than 500 masl equal to 5,34 ton/ha.

TREND DAN ESTIMASI PRODUKSI PADI DAN KONSUMSI BERAS DI PROVINSI KALIMANTAN TENGAH

This study is conducted to determine the trend and estimation of rice production and rice consumption in Central Borneo Province. The study is held in Central Borneo Province by using secondary data from October 2017 to March 2018, using Least Square’s method. The results shows that the trend of rice production in Central Borneo Province in 2001-2015 is increasing. Same as paddy production which is increasing almost every year, rice production increasing almost every year. Estimation rice production in Central Borneo Province in 2016-2020 is also known that five years ahead, rice production will be increasing from year to year. Trend of rice consumption in Central Borneo Province in 2001-2015 is increasing every year, and Estimation of rice consumption in Central Borneo Province in 2016-2020 is known that five years ahead will be increasing from year to year although it can still be balanced with the value of production which is greater than consumption. Even the province of Central Borneo is also potential to surplus of rice till the year estimated 2020, that is 329.255,83 tons, so till 2020 rice production Central Borneo Province still can be sufficient for consumption need of the population.

Pengembangan Penggunaan Penginderaan Jauh untuk Estimasi Produksi Padi (Studi Kasus Kabupaten Bekasi)

Pemanfaatan produk penginderaan jauh satelit Landsat-8 (OLI) untuk melakukan pendugaan luas area panen dan produktivitas tanaman padi dengan menggunakan parameter Enhanced Vegetation Index (EVI) merupakan salah satu pendekatan baru untuk menghasilkan data estimasi produksi padi wilayah. Berdasarkan hasil analisis citra satelit dengan tanggal akuisisi bulan Mei-Agustus 2015, diperoleh hasil perkiraan luas panen padi sawah di Kabupaten Bekasi periode bulan Juli-Oktober 2015 adalah seluas 15.86 ribu ha atau lebih kecil 7.74 (32.79 %) ribu ha dibandingkan angka BPS pada periode yang sama. Berdasarkan keeratan hubungan antara nilai produktivitas hasil ubinan BPS dengan nilai EVI maksimum, diperoleh model persamaan pendugaan produktivitas tanaman padi sawah sebagai berikut: Produktivitas (ku ha-1) = 36.818 + 44.965 EVI maksimum. Nilai Rsquare yang diperoleh sebesar 0.809. Berdasarkan model tersebut diperoleh pendugaan produktivitas padi sawah di Kabupaten bekasi periode bulan Juli-Oktober 2015 sebesar 47.40 ku ha-1 atau lebih kecil 12.66 ku ha-1 dibandingkan angka produktivitas subround I 2015, lebih kecil 6.77 ku ha-1 dibandingkan angka produktivitas subround II 2015, lebih kecil 10.15 ku ha-1 dibandingkan angka produktivitas subround III 2015, dan lebih kecil 6.62 ku ha-1 dibandingkan angka produktivitas periode Januari-Desember 2015 yang dipublikasikan BPS. Sementara itu, perkiraan produksi padi sawah periode panen bulan Juli- Oktober 2015 berdasarkan analisis citra satelit yakni sebanyak 75.16 ribu ton GKG atau lebih kecil 55.35 ribu ton GKG (42.41%) dibandingkan angka yang dipublikasikan BPS pada periode yang sama.
 Kata kunci: Enhanced Vegetation Index, Landsat-8 (OLI), estimasi produksi padi

ESTIMASI PRODUKSI PADI DENGAN ANALISIS CITRA SATELIT LANDSAT 8 DI KABUPATEN KLUNGKUNG PROVINSI BALI

Rice crop is one of the important commodities that must always be available, so estimation of rice production becomes very important to do before harvesting time to know the food availability. The technology that can be used is remote sensing technology using Landsat 8 Satellite. The aims of this study were (1) to obtain the model of estimation of rice production with Landsat 8 image analysis, and (2) to know the accuracy of the model that obtained by Landsat 8. The research area is located in three sub-districts in Klungkung regency. Analysis in this research was conducted by single band analysis and analysis of vegetation index of satellite image of Landsat 8. Estimation model of rice production was developed by finding the relationship between satellite image data and rice production data. The final stage is the accuracy test of the rice production estimation model, with t test and regression analysis. The results showed: (1) estimation of rice production can be calculated between 67 to 77 days after planting; (2) there was a positive correlation between NDVI (Normalized Difference Vegetation Index) vegetation index value with rice yield; (3) the model of rice production estimation is y = 2.0442e1.8787x (x is NDVI value of Landsat 8 and y is rice production); (4) The results of the model accuracy test showed that the obtained model is suitable to predict rice production with accuracy level is 89.29% and standard error of production estimation is + 0.443 ton/ha. Based on research results, it can be concluded that Landsat 8 Satellite image can be used to estimate rice production and the accuracy level is 89.29%. The results are expected to be a reference in estimating rice production in Klungkung Regency.

アジア水稲作況作成のための農業気象提供システムの構築

The Asia Rice Crop Estimation and Monitoring (Asia-RiCE) initiative was launched by Asian space and agricultural agencies in 2012. The initiative promotes strengthening rice monitoring abilities through the use of remote sensing tools, and disseminating relevant, timely and accurate estimation of rice production. In this study, we develop a web-based system (JAXA's Satellite-based Monitoring Network system : JASMIN) to monitor the agrometeorological conditions of paddy areas in Southeastern Asia. JASMIN collects various satellite-derived agrometeorological data, provided by individual servers or websites, and then reprocesses the data spatially and temporally so that it can be used in the initiative. This system provides current and anomalous condition maps, as well as time-series chart in agrometeorological data such as soil moisture, solar radiation, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), precipitation, and drought index, via the JASMIN website. This satellite-derived information, updated twice a month, is currently used to produce rice growth outlook for Indonesia, Thailand, Philippines, and Vietnam, as part of the Asia-RiCE initiative.

Risk Assessment, Partition and Economic Loss Estimation of Rice Production in China

Agricultural risk, especially the risk assessment, partition and economic loss estimation of specific and main crops, maize, wheat and rice, is widely touted in China as a means of improving the effective productivity. The main objective of this article is to perform a detailed analysis of the stability and comparative advantage of rice production in 30 provinces on the basis of relative rice production data from 2000 to 2012 in China. The non-parametric information diffusion model based on entropy theory was used to assess rice production risk. Accordingly, we divided the risk level with hierarchical cluster analysis. Then, we calculated the economic loss of rice production by the scenario analysis. The results show that, firstly, the national disaster risk of rice production is at a lower level. Secondly, there are significant differences in the stability, comparative advantage and risk probability of rice production among the 30 provinces. Thirdly, Shanxi province belongs to the high risk zone, 12 provinces belong to the middle risk zone and 17 provinces to the low risk zone. Finally, there is a proportional rate between the economic loss (yield loss) and disaster area (yield loss rate) of rice production. Therefore, we could obtain some significant policy suggestions.

Detection of Rice Distribution Using NDVI Time-Series Similarity Measure

Rice cultivation is essential for agricultural production in Mekong Delta. Rice production estimation is based on the measurement of paddy field area and plantation system. Detection of paddy rice area and the plantation system is significant to food security decision. Based on the MODIS NDVI time-series data and the Dynamic Time Wrapping (DTW) similarity measure we detected the single and multiple rice distribution in Mekong Delta in 2010. Firstly, a combination of replacement and filter method was applied to denoising the MODIS NDVI time-series. Secondly, extracted the standard NDVI time-series cycle of single and multiple rice on MODIS NDVI time-series image. Thirdly, compare each pixel’s NDVI time-series cycle with each standard NDVI time-series cycle based on DTW distance. Finally, choose threshold in DTW distance and determine paddy rice’s distribution. Results were examined by sampling point and high resolution image of Mekong Delta; it shows that the extraction accuracy is 86%, relatively high for MODIS data classification outcome.

Estimation of rice production at regional scale with a Light Use Efficiency model and MODIS time series

This study presents the application of a Light Use Efficiency model that exploits MODIS satellite data to assess rice yield in Italy. Field experimental data acquired in 2003 and 2004 were used to define model’s parameters (Radiation Use Efficiency and Harvest Index) and to calibrate the relation between MODIS-EVI and the fraction of Absorbed Photosynthetically Active Radiation (fAPAR). Satellite images were used to spatially estimate fAPAR and key phenological rice stages that control biomass accumulation. The maps of rice yield estimates for the year 2002, 2003 and 2004 were compared to official statistics and showed a good agreement with an inter-annual relative RMSE ranging from 15 to 17%.

Advance estimation of rice production in India using weather indices

Estimation of crop production from area and yield estimates based on sample surveys and crop-cutting experiments, an approach currently being followed in India, can give final estimates only after the crops are actually harvested. However, various policy decisions related to pricing, marketing, export/import, distribution, etc., necessitate the use of advance estimates. A simple model based on weather indices can be used to obtain accurate estimates of annual rice production even before the crop is harvested. Crop production in India largely depends on the summer monsoon (June to September), which provides about 80–90% of annual rainfall in most parts of the country. The summer monsoon rainfall is the main source of water for the kharif crop. Rainwater stored as soil moisture and other water resources (lakes, reservoirs, and rivers) are also important for the– rabi– and other irrigated crops. Parthasarathy et al (1988) have estimated the annual food-grain production in the country using the all-India summer monsoon rainfall data. The rainfall data, however, showed significant variations in time and space. To obtain more accurate production estimates, the temporal and spatial variations of monsoon rainfall were also included in the model through suitable weather indices.

Rice monitoring and production estimation using multitemporal RADARSAT

Rice monitoring and production estimation has special significance to China, as rice is the staple grain and accounts for 42% of the crop production in this country. Radar remote sensing is appropriate for monitoring rice because the areas where this crop is cultivated are often cloudy and rainy. Synthetic Aperture Radar (SAR) is thus anticipated to be the dominant high-resolution remote sensing data source for agricultural applications in tropical and subtropical regions. It also provides revisit schedules suitable for agricultural monitoring. This paper presents the results of a study examining the backscatter behavior of rice as a function of time using multitemporal RADARSAT data acquired in 1996 and 1997. A rice-type distribution map was produced, showing four types of rice with different life spans ranging from 80 days to 120–125 days. The life span of a rice crop has significant impact on the yield, as well as on the taste and quality of the rice, with the longer growing varieties having the best taste and the highest productivity. The rice production of three counties and two administrative regions, totaling 5000 km 2, was estimated in this study. The accuracy of the rice classification was found to be 91% (97% after postclassification filtering) providing confidence that multitemporal RADARSAT data is capable of rice mapping. An empirical growth model was then applied to the results of the rice classification, which related radar backscatter values to rice life spans. These life spans could then be used to sum up the production estimates, which were obtained from agronomic models already in use for rice by local agronomists. These models related the yield of rice to their life span based on empirical observations for each type of rice. The resulting productivity estimate could not be compared to any other existing data on yield production for the study-area, but was well received by the local authorities. Based on the studies carried out in the Zhaoqing test site since 1993, it is suggested that rice production estimates require three radar data acquisitions taken at three different stages of crop growth and development. These three growth stages are: at the end of the transplanting and seedling development period, during the ear differentiation period, and at the beginning of the harvest period. Alternatively, if multiparameter radar data is available, only two data acquisitions may be needed. These would be at the end of the transplanting and seedling development period, and at the beginning of the harvest period. This paper also proposes an operational scenario for rice monitoring and production estimation.