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The purpose of this paper is to analyse the effect of enhanced knowledge and technology innovations, which were resulted from training, on the simultaneous production of rice and soybean in Java, Indonesia.

A model of product possibility frontier with two outputs produced using the same resources was employed. Based on the model, supply responses of soybean and rice were derived. Aggregate data consisting of 12 regions during the ten years of 2000–2009 were compiled from relevant agricultural institutions at the provincial level.

Improvement in farmers’ capacity has been able to increase production of soybean and rice simultaneously. Farmers’ capacity increased after completion of the school. Knowledge and skill gained from the school have been applied to both rice and soybean farming. Other economic factors also affected the supply response of both commodities.

Available data covered periods 2000–2009. However, the outcomes are still relevant to the current situation because food crops are the basic necessity. This study used secondary aggregate data, which might be less accurate than primary data. However, secondary data have the advantage concerning coverage and time span.

The Government, in collaboration with non-government organisations and the private sectors, should continue to enhance farmers’ capacity to increase the production of food crops to feed people in Indonesia, and over the world in general.

An analysis of joint production using a concept of product transformation curve can measure the impact of training on multi products.

The purpose of this paper is to investigate the causal relationship between the prices of rice, crude oil, wheat, corn and soybean in China and estimate the long-run and short-run price relationships.

Using monthly price date over the period of January 1998-December 2013 in China, this paper employs an autoregressive distributed lag (ARDL) bounds test to explore the cointegration relationship among the price variables and estimate the ARDL long-run price relationship and the short-run error correction process (ARDL-EC).

The empirical results indicate that crude oil, as one of the forcing variables along with wheat, corn, and soybean prices, is effecting rice price in China. Both the long-run and short-run price transmission elasticity estimates suggest the importance of crude oil price on the formation of rice prices. Furthermore, the adjustment speed coefficient is found to be statistically significant, supporting the notion that there is an error correction mechanism for maintaining the long-run price relationship facing short-run shocks.

This paper adopts four types of commodity food prices to explore the relationships with crude oil price. The evidence of market integration, including the degree of price transmission and the speed of adjustment, remains a crucial step to proceed with the government intervention.

This paper seeks to study the impact of bio‐fuel policies on oil and food futures prices from December 6, 2004 to August 1, 2008.

The daily closing prices of brent crude oil, light sweet crude oil, corn, wheat, soybeans, and rough rice futures from December 6, 2004 to August 1, 2008 are used in this research. The vector error correction model is applied in order to study the impact of bio‐fuel policies on oil and agricultural futures prices.

Unit root and cointegration tests show that the brent crude oil, light sweet crude oil, wheat, corn, soybeans, and rough rice futures are stationary and have a long‐run equilibrium relationship. Granger causality tests of the four periods shows that the causality relationship between oil futures and food futures changes over time. The first period result shows many Granger causes on several variables at a 5 percent significance level. The second period has more Granger causes at the 5 percent significance level. However, the Granger causality relationships become fewer and fewer in the third and fourth period.

This is the first paper to study the impact of the four major bio‐fuel policies of Brazil, the European Union, and the USA.

This paper aims to investigate the impacts of climate change on the People's Republic of China's (PRC) grain output using rural household survey data. The paper highlights the regional differences of impacts by estimating output elasticities (with respect to climate change) for different grain crops and different regions.

The paper uses production function to investigate the responses of grain output to climate variables as well as other traditional input variables. The use of production function approach allows us to do away with the competitive land market assumption as required in the Ricardian approach. The paper will use interaction terms of climate variables and regional dummies to capture the regional differences of climate change impact on grain crops.

The results indicate that the overall negative climate impacts on the PRC's grain output range from −0.31 to −2.69 percent in 2030 and from −1.93 to −3.07 percent in 2050, under different emission scenarios. The impacts, however, differ substantially for different grain crops and different regions.

This paper addresses the limitations of existing literature by highlighting regional differences and crop varieties using the most recent nationwide rural household survey data. The results indicate pronounced regional differences and crop differences in the impacts of climate changes on PRC's grain output.

The purpose of this paper was to analyse the present status of the information available on rice bean [Vigna umbellata (Thunb.)] cultivars with respect to its nutritional and functional characteristics. It is one of the non-conventional and under-utilized beans of South and Southeast Asia. In northeastern regions of India, it is an important crop for shifting cultivation or kitchen garden which is consumed as vegetable (tender pod) and pulse. The information gaps filling could be useful for several work proposals and for consumers and cultivators to know the nutritional strength and varied usability of the grain which in-turn can help in its commercialization.

The approach to information collection was emphasized on nutritional strength of the grain compared to other beans of Vigna family and the varied usability among the rice bean varieties of the Himalayan Belt.

The average carbohydrate, protein, fat, crude fibre and ash content of 75 genotypes of rice bean was found 59.96 ± 2.81, 20.78 ± 2.65, 1.74 ± 1.42, 5.07 ± 1.17 and 4.44 ± 1.03 per cent, respectively. It has high quality protein with all essential amino acid in balance manner. Among the minerals, it contains calcium, iron, zinc and potassium with high bio-availability of calcium (59.8 ± 0.3 per cent). The factors such as flatulence producing saccharides, trypsin inhibitors, polyphenols, phytic acid, saponin and tannins of rice bean were reported to be in the range of 3.28-5.87 per cent, 55.12-163.98 TIU/g, 0.58-1.82 1.88-8.17, 1.2-3.1 and 0.24-1.55 per cent, respectively. The toxic compounds such as hydrogen cyanide (0.09 mg/100 g) and L-3, 4-dihydroxyphenylalanine (0.36 mg/100 g) is lowest in rice bean. The nutritional advantages of rice bean over the other beans of Vigna family were required to emphasize to induce interest among the researchers, producers and consumers.

In view of the recommended daily requirements with respect of amino acids, vitamin-B and minerals, rice bean could be a major component of the diet to fulfill them. However, the scanty information available on nutritional and functional components of rice bean varieties grown in northeastern India calls for the gap filing research to explore full potential of the bean.

The information about differences among the northeastern varieties of rice bean available on nutritional functional components essentially required the updating of research data to explore full potential and utility of the bean.

The cross-quantilogram analysis is employed. The latter can assess the temporal association between two stationary time series at different parts of their joint distribution. Data are daily prices and trading volumes from the futures markets of five agricultural commodities, namely, corn, hard red wheat, oats, rice and soybeans.

The objective to the present work is to investigate for directional predictability between returns and volume (and vice versa) in the futures markets of agricultural commodities.

The empirical results reveal evidence, weak as well as strong, that extreme low values of returns are likely to lead high levels of volume. There is also weak evidence that extreme low values of volume are likely to precede high values of returns, except for the futures markets of oats where there is very strong evidence that low values of volume are likely to lead high values of returns. For the commodity of soybeans, there is very strong evidence that extreme high levels of volume are likely to lead high values of returns, but they are very short lived.

Agricultural futures have been recently characterized by increased volatility leading hedgers to be looking for diversification. The present findings suggest that when price crashes occur, investors who suffer losses wish to sell, increasing this way the trading activity. Concurrently, the results reveal that extreme low levels of trading volume might signal a possible price turn around for traders.

This is the first study that employs the quantilogram approach in order to investigate for potential predictability from returns to volume and from volume to returns, in the futures markets of agricultural commodities.

The purpose of this paper is to demonstrate how crop price insurance premium can be calculated using an option pricing model and how insurers can transfer underwriting risks in the futures market.

Based on data from spot and futures market in China, this paper develops an improved B-S model for the calculation of crop price insurance premium and tests the possibility of hedging underwriting risks by insurance firms in the futures market.

The authors find that spot price of crops in China can be estimated with agricultural commodity futures prices, and can be taken as the insured price for crop price insurance. The authors also find that improved B-S model yields better estimation of crop price insurance premium than traditional B-S model when spot price does not follow geometric Brownian motion. Finally, the authors find that hedging can be one good alternative for insurance firms to manage underwriting risks.

This paper develops an improved B-S model that is data-driven in nature. Insured price of the crop price insurance, or the exercise price used in the B-S model, is estimated from a co-integration model built on spot and futures market price series. Meanwhile, distributional patterns of spot price series, one important factor determining the applicability of B-S model, is factored into the improved B-S model so that the latter is more robust and friendly to data with varied distributions. This paper also verifies the possibility of hedging of underwriting risks by insurance firms in the futures market.

The paper aims to measure the effects of market liberalization on Chinese farmers' crop planting decisions.

The effects are measured using a censored, two‐stage, least‐square regression.

The results show that the effects of market liberalization on planting decisions are more significant in the case of crops with minimum support price (rice, wheat, and corn) than in the case of crops where planting decision is determined by market prices (cotton and soybean). The effects appear to be different across regions and time zones and more significant in 1993 than in 2005.

The result suggests that market liberalization along the past ten years achieved significant effects in Chinese farmers planting decision. This outcome should be taken into consideration when evaluating and implementing future Chinese agricultural policy income‐based interventions as a means to meet domestic food security goals and increase farmers' income level.

The purpose of this paper is to comprehensively and accurately analyze the supply and demand structural balance of grain in the context of China's agricultural supply-side reform. By subdividing and forecasting the supply and demand, it is beneficial for targeted production in the case of clear demand and supply trends of main grain varieties.

This paper forecasted and analyzed the demand of main grain varieties by the grey interval forecast, and based on the grey incidence analysis of more influence factors, forecasted the grain production with GM (1,N) model.

The results show that the demand and yield will keep sustainable growth in the next three years, while there is still a big gap between the supply and demand of the main grain varieties in China and the soybean's production is far behind the growing demand.

This paper can make full use of the information to provide the evidence for government to formulate policies and put forward some correlative suggestions for growers.

In this paper, the grey model technology is applied to the structural reform of grain supply side, and different models are used to predict the structural balance of supply and demand of different kinds of grain. The study of grain supply and demand structural balance in China is vital to ensure grain security in the context of agricultural supply-side reform.

The purpose of this paper is to investigate changes proposed in 2012 to commodity programs for the new Farm Bill. Both the Senate and House Agriculture Committee versions of the new Farm Bill eliminate current commodity programs including direct payments, create new revenue‐based commodity program options designed to cover “shallow” revenue losses, and also introduce supplemental crop insurance coverage for shallow revenue losses.

This paper documents the payment functions for the new revenue programs proposed in both the Senate and House Ag Committee Farm Bills, and also estimates expected payments for each using a model based on historical county yield data, farmer‐level risk rates from RMA, and commodity price levels from the March 2012 CBO baseline projections.

The authors find significant variation in expected per acre payment across programs, crops, and regions. In general, the Senate's bill would be expected to be preferred over the House's bill for corn and soybean producers, particularly those in the Midwest. Also, the RLC program in the House's Bill typically would be projected to pay much less than the Senate's SCO or ARC programs for most producers in the Midwest.

This study develops an extensive nationwide model of county and farm yield and price risks for the five major US crops and employs the model to evaluate expected payment rates and the distribution of payments under the House and Senate Farm Bill proposals. These analyses are important for program evaluation and should be of great interest to producers and policymakers.