An Econometric Model for Nigeria’s Rice Market

A dynamic econometric model of Nigeria’s rice market was designed to serve as a base for future policy analyses. Using time-series data spanning 38 years, the model contains four structural equations representing paddy area harvested, paddy yield, per capita demand, and producer price variables. Estimates for these equations were obtained using the autoregressive distributed lag (ARDL) cointegration approach. Results of the paddy production and yield sub-models showed that paddy area harvested, and paddy yield was price inelastic. Furthermore, the paddy area harvested responded favourably to technological advancement. For the demand sub-model, estimated own price and cross-price elasticities showed that rice has an inelastic demand response, with wheat being a substitute. A series of validation tests strengthened the reliability of the model for use as an empirical framework for forecasting and analysing the effects of changes in policies such as rice import tariff reforms on production, consumption, retail price, and imports.

Segmentasi Pertumbuhan Padi berbasis Aerial Image menggunakan Fitur Warna dan Tekstur untuk Estimasi Produksi Hasil Panen

<p class="Abstrak">Pertumbuhan padi di daerah yang luas seringkali tidak ideal. Ini dapat disebabkan oleh faktor alam, jenis varietas padi, dan model perawatan yang digunakan. Ini juga akan mempengaruhi hasil panen. Luasnya lahan membuat petani sulit untuk memantau bagian yang tidak terjangkau. Seringkali pemantauan perkembangan padi dilakukan di tepi sawah tetapi tidak mencapai area tengah. Studi ini mengusulkan sistem pemantauan untuk pengembangan padi yang dapat menjangkau secara lebih luas dan memperkirakan hasil padi di setiap area lahan pertanian. Sistem ini menggunakan gambar udara untuk menjangkau area yang lebih luas dan kemudian memperkirakan produksi pertanian. Estimasi produksi dilakukan dengan mengelompokkan gambar kawasan pertanian menggunakan metode K-Means. Pengelompokan ini menggunakan parameter warna HSV dan tekstur Gabor sebagai fitur dari setiap bagian gambar. Hasilnya adalah segmen area padi berdasarkan pertumbuhannya. Jumlah segmen yang sesuai dengan usia Padi nyata akan menentukan nilai estimasi hasil. Penelitian menunjukkan bahwa tiga segmen pengembangan padi, dan memperkirakan produksi adalah 1.787 ton dengan perkiraan panen maksimum 1.924 ton dari data nyata 1,80 ton. Dan dengan skala kesalahan persentase rata-rata absolut 0,72% dan perbedaan 0,013 ton.</p><p class="Abstrak"> </p><p class="Abstrak"><em><strong>Abstract</strong></em></p><p><em>Paddy growth in large areas is often not ideal. This can be caused by natural factors, types of rice varieties, and the treatment model used. This will also affect crop yields. The extent of land makes it difficult for farmers to monitor the unreachable part. Often monitoring of rice developments is done on the edge of the field but does not reach the middle area. This study proposes a monitoring system for rice development that can reach more broadly and estimate the yield of rice in every area of agriculture land. This system uses aerial images to reach a wider area and then estimates of agricultural production. Estimation of production is done by clustering images of agricultural areas using the K-Means method. This clustering uses HSV color parameters and Gabor textures as features of each part of the image. The result is a segment of the paddy area based on its growth. The number of segments corresponding to the age of the real Paddy will determine the estimated value of the yield. The research shows that three segments of rice development, and estimates the production is 1,787 tons with a maximum estimated harvest of 1,924 tons from the real data of 1, 80 tons. And with a mean absolute percentage error scale of 0.72% and a difference of 0.013 tons.</em></p><p class="Abstrak"><em><strong><br /></strong></em></p>

A New Approach of Ensemble Learning Technique to Resolve the Uncertainties of Paddy Area through Image Classification

Remote sensing technology has rendered lots of information in agriculture. It has usually been used to monitor paddy growing ecosystems in the past few decades. However, there are uncertainties in data fusion techniques which can be resolved in image classification on paddy rice. In this study, a series of learning concepts integrated by a probability progress Fuzzy Dempster-Shafer (FDS) analysis is presented to upgrade various models and different types of image data which is the goal of this study. More specifically, the study utilized the FDS to generate a series of probability models in the classification of the system. In addition, Logistic Regression (LR), Support Vector Machine (SVM), and Neural Network (NN) approaches are employed into the developed FDS system. Furthermore, two different image types are Satellite Image and Aerial Photo used as the analysis material. The overall classification accuracy has been improved to 97.27%, and the kappa value is 0.93. The overall accuracy of the paddy field image classification for a multi-period of mid-scale satellite images is between 85% and 90%. The overall accuracy of the classification using multi-spectral numerical aerial photos can be between 91% and 95%. The FDS improves the accuracy of the above image classification results.

Perilaku Solusi Pada Laju Pertumbuhan Jumlah Benih Padi Serta Luasan Area Sawah Desa Baratan

The aims research to find out the stages of good rice seed will be planted, to know the time calculation in Java to plant rice, and to complete the model and general mathematical solution by calculating the number of rice seeds involving the area of rice fields (10.000 m ^ 2). This type of research used qualitative research with ethnography approach. Research subjects namely Mbah and Pakde in Baratan village who work a rice farmer. This study uses interview guidelines. The results obtained from the interview data that is to determine the stage of good rice seed will be planted is to plow the field using a hoe, soaking rice seeds for 48 hours, sort the results of good rice immersion, curing for 48 hours to germinate, sow seeds evenly, after 20 days of paddy seedlings removed and planted, 1 week given urea fertilizer, 45 days fertilized again, and 3 months ready for harvest, while for the calculation of time in good Java language to plant rice that is prey ketelu on 25 August - 18 september (24 days ). The result of the mathematical model and solution associated with calculating the amount of rice involving the paddy area is obtained by the area of rice field (10.000m 2), the number of rice seeds needed is more.