Forecasting food production is important to identify possible shortages in supply and, thus, food security risks. Such forecasts may improve input allocation decisions that affect agribusiness and the input supply industry. This paper explains methods and data used to forecast acreage of four crops that are particularly important staple commodities in the world, namely wheat, corn, rice, and soybeans for major global producer countries. It focuses on forecasting acreage-one of the two major determinants of grain production-3 months before planting starts with publicly available data. To this end, we use data from the period 1991 to 2013 and perform an out-of-sample forecast for the year 2014. A particular characteristic of this study is that the respective acreage determinants for each country and each crop are identified and used for forecasting separately. This allows accounting for the heterogeneity in the countries' agricultural, political, and economic systems through a country-specific model specification. The performance of the resulting forecasting tool is validated with ex-post prediction of acreage against historical data.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Tables Table 1 Correlation matrix of different prices for wheat and rice p.22 Table 2 Regressions for wheat production p.26 Table 3 Regressions of rice production p.27 Table 4 Procurement regression estimates p.30 Table 5 Regressions of wheat demand p.37 Table 6 Regressions of rice demand p.38 Table 7 Results of the public October stock level estimation p.46 Table 8 Regression for the private rice stocks p.48 Table 9 Foreign trade regression estimates p.52 The study revealed the strong impact of policy measures on production, procurement, stocks and trade. We detected several market distortions and mounting fiscal costs. Terms of use: Documents in EconStor mayWheat and rice supply strongly and significantly respond to the minimum support price (MSP). Wholesale prices at planting or lagged harvest time prices are largely irrelevant for production. The Food Corporation of India's (FCI's) procurement volume is driven by the production level and the difference between the MSP and market price. The signs of the estimated price elasticities of demand are consistent with the theory; however, for rice they turned out to be insignificant. Rice consumption turned out to be strongly influenced by distribution of subsidised rice, which can be linked to high coverage and subsidy under the public distribution system (PDS). In the case of wheat, the influence of subsidised wheat is much less pronounced, probably due to weaker functioning of the PDS for this grain.The public stock analysis suggests higher storage losses for rice (10 per cent) than for wheat (2 per cent). Public stocks are found to strongly crowd out private stocks but not to the same extent as public stocks are build up. Total exports are highly distorted by trade regulations.Therefore, there was no correlation detected between exports and domestic vs.international price difference in the case of rice and in the case of wheat, the correlation turned out to be contrary to theoretical expectations. The analysis of intra-year data revealed strong seasonality in prices, procurement and stock levels, in particular, for wheat (this was lower for rice).Starting from 2006-07, there is a clear upward trend in inflation adjusted costs of operating the public food procurement and distribution system, mainly because of rising procurement volume and MSPs. On the other hand, revenue has declined in real terms, due to lower real central issue prices and only marginal revenue from domestic sales and exports. As a...

We analyse current and possible future reforms of the Indian food policies for the most important staple grains, wheat and rice, within a two-commodity dynamic partial equilibrium model with stochastic shocks. The model is empirically grounded and reproduces past values well. It uses a new reduced-form approach to capture private storage dynamics. We evaluate the implementation of the National Food Security Act (NFSA) under several policy measures with the current regime as well as two scenarios with a regime change -implementation of cash transfers and deficiency payments. Implications for market fundamentals and fiscal costs are simulated in the medium term -until 2020/21. The NFSA puts a high pressure on fiscal costs and public stocks. Relying on imports with low support prices results in low fiscal costs and stable, but higher domestic and international prices, and a high risk of zero stocks. A policy strategy to manipulate procurement prices in order to maintain public stocks close to the norms leads to slightly higher fiscal costs with lower, but more volatile prices. The highest domestic price volatility occurs under a strategy which uses export bans in order to maintain sufficient public stocks. A cash-based regime can bring considerable savings and curb fiscal costs, particularly 1 Marta Kozicka is a Senior Researcher at

Unexpected high and volatile food prices during the 2007-2008 world food crisis and thereafter have reemphasized the question of how countries can protect themselves from supply shortages. In view of the various trade restrictions imposed by some major exporting countries, governments tend once again to focus more on self-sufficiency and food storage. Additionally, emerging economies like China aim at increasing their yields. This is because the possibilities of expanding agricultural land are limited, while population, total grain demand, and meat consumption are rising.The primary purposes of analyzing the supply response are threefold in this chapter. First, this work aims to identify the different factors that can affect production, such as market prices, biophysical conditions, and infrastructure. The second objective is to analyze the differences in the effects of these factors on the different crops. The third aim is to evaluate how the predictive power of prices evolves over time and therefore to understand when farmers react most strongly to prices. Hence, a clear understanding of the farmers' planting and production behavior is needed.

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