Global Agricultural Supply and Demand: Factors Contributing to the Recent Increase in Food Commodity Prices

Artificial neural network is an information processing paradigm that is inspired by biological neural cell systems, like the brain, that processes information. The purpose of this research is to develop neural networks to predict the price of food commodities using backpropagation method. The research was conducted by using the rate of monthly price of food commodities in Palu from January 2011 - December 2015. The data is used to predict food commodity prices forduring 2016. The backpropagation networks consists of three layers. The first layer of input is constructedin the form of monthly prices of IR 64, ciherang, membramo, cimandi, superwin, sintanur, cisantana, sticky black, sticky white, yellow corn dry, white corn, soybeans, peanuts, green beans, cassava, sweet potato, onion, garlic, red pepper large, red pepper curls, cayenne pepper, cabbage round, potatoes, tomatoes, carrots, cauliflower, beans, onion, avocado, red apples, green apples, oranges, jackfruit, mango, pineapple, papaya, banana, banana horns, rambutan, bark, olive, durian, watermelon, and mangosteen from January – December that consist of 12 variables. One hidden layer consistof five neurons and the other one is the output, that is the food commodity prices. The training process shows that on a maximum iterations on 500, constant learning rate 0,3 and 0,6 momentum, the predictions have 97.92% of level accuracy. The identification resultof food commodity prices behavior in Palu is predicted as follow: IR 64 Rp7.387, ciherang Rp8.182, membramo Rp8.150, cimandi Rp8.131, superwin Rp8.228, sintanur Rp8.660, cisantana Rp8.122, black sticky rice Rp21.383, white sticky rice Rp16.558, dry yellow corn Rp5.983, white corn Rp9.283, soybeans Rp14.600, peanuts Rp20.008, green beans Rp16.375, cassava Rp8.225, sweet potato Rp8. 542, red onion Rp28.550, garlic Rp21.208, red chili Rp27.308, curly red chili Rp23.650, cayenne Rp36.450, round cabbage Rp6.833, Rp12.067 potatoes, tomatoes Rp6.108, carrots 11.000, cauliflower Rp8.625, beans Rp10.333, scallion Rp25.242, avocado 11.000, red apple Rp29.023, green apple Rp31.067, orange Rp6.083, jackfruit Rp23.483, mango Rp11.187, pineapple Rp8.183, papaya Rp10.600, bananas Rp8.481, horn banana Rp2.683, rambutan Rp8.450, barking Rp5.625, tan Rp8.366, durian Rp19.208, watermelon Rp14.528 and mangosteen Rp18.067. It is predicted that the food commodity prices increased monthly.

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Are Agricultural Commodity Prices on a Conventional Wisdom with Inflation?

SAGE Open ◽

10.1177/21582440211038347 ◽

2021 ◽

Vol 11 (3) ◽

pp. 215824402110383

Author(s):

Ting-Ting Sun ◽

Chi-Wei Su ◽

Ran Tao ◽

Meng Qin

Keyword(s):

Mutual Influence ◽

Supply And Demand ◽

Interaction Mechanism ◽

Commodity Prices ◽

Chinese Government ◽

Agricultural Commodity ◽

Negative Effects ◽

Positive Effects ◽

Price Levels ◽

Causality Tests

The study investigates the mutual influence between agricultural commodity prices (ACP) and inflation (INF) in China by employing the bootstrap full- and sub-sample rolling-window Granger causality tests. We find that ACP has positive effects on INF, indicating that agricultural commodities play a significant role in stabilizing general price levels, but the higher ACP may create inflationary pressures. However, the negative effects suggest that under the shock of external uncertainty, the rise of ACP is not always regarded as the prime driver of INF. The results are not consistent with Hypothesis 1, which highlights that INF is positively affected by ACP. In turn, we also find positive and negative impacts of INF on ACP, showing that the level of INF can affect the supply and demand of agricultural commodity markets, it can be considered as a factor affecting ACP. The findings support Hypothesis 2 derived from the interaction mechanism. These analyses can assist the Chinese government to understand that ACP is not an effective indicator for forecasting INF. It also can prompt them to pay attention to the transmission effect of price levels on ACP, to maintain the stability of the agricultural commodity market.

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Food Commodity Prices Volatility: The Role of Biofuels

Natural Resources ◽

10.4236/nr.2014.55019 ◽

2014 ◽

Vol 05 (05) ◽

pp. 200-212

Author(s):

Christopher L. Gilbert ◽

Harriet K. Mugera

Keyword(s):

Commodity Prices ◽

Food Commodity

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GLOBAL CRUDE OIL PRICE AND DOMESTIC FOOD COMMODITY PRICES: EVIDENCE FROM INDONESIA

Buletin Ilmiah Litbang Perdagangan ◽

10.30908/bilp.v14i1.406 ◽

2020 ◽

Vol 14 (1) ◽

pp. 95-120

Author(s):

Tiara Kencana Ayu

Keyword(s):

Panel Data ◽

Crude Oil ◽

Local Food ◽

Price Change ◽

Commodity Prices ◽

Oil Prices ◽

Jel Classification ◽

Crude Oil Prices ◽

The Impact ◽

Food Commodity

Abstrak Penelitian untuk menganalisis hubungan antara harga minyak dunia dan harga komoditi pangan di pasar domestik masih jarang ditemukan. Dengan membuat Model Panel Data dari 34 provinsi di Indonesia pada tahun 2010-2017, penelitian ini bertujuan untuk menginvestigasi pengaruh perubahan harga minyak dunia terhadap beberapa harga komoditi pangan lokal (kedelai,import, kedelai lokal, beras lokal, dan jagung lokal). Hasil penelitian ini mengindikasikan bahwa harga minyak dunia dapat memengaruhi harga pangan lokal di Indonesia melalui tingginya biaya pengiriman pada aktivitas impor. Selain itu, harga komoditi pangan dunia juga terbukti dapat memengaruhi harga seluruh komoditi pangan lokal yang diteliti, yang mengimplikasikan bahwa harga komoditi pangan di Indonesia dipengaruhi oleh kondisi pasar internasional. Hasil penelitian ini memberikan masukan bagi pembuat kebijakan di Indonesia untuk mempertimbangkan perubahan harga minyak dunia dan harga komoditi global dalam menstabilkan harga komoditi lokal di Indonesia, terutama komoditi yang diimpor. Abstract Globally, studies examining the nexus between global crude oil prices and food commodity prices in domestic markets are scant. Employing a panel data model of 34 provinces in Indonesia from 2010 - 2017, this study investigates the impact of global crude oil’s price change on some local food commodity prices (imported soybean, local soybean, local rice, and local maize). Previous studies found that local food commodity prices in some countries were not affected by global crude oil prices; however, this study, by controlling other factors which could affect local commodity prices, finds different results. This study’s findings indicate that global crude oil prices could affect Indonesia’s local commodity prices due to higher shipping costs in import activity. In addition, global commodity prices are also proved to affect all commodities examined in this study, which implies that local food commodity prices in Indonesia are influenced by the international market. This study provides input to policymakers in Indonesia to consider the movement of global crude oil prices and global commodity prices in stabilizing local food commodity prices in Indonesia, especially the imported commodities. JEL Classification: F15, O13, Q11

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