Corn Prices Research Articles

Climate and environmental data contribute to the prediction of grain commodity prices using deep learning

AbstractBackgroundGrain commodities are important to people's daily lives and their fluctuations can cause instability for households. Accurate prediction of grain prices can improve food and social security.Methods & MaterialsThis study proposes a hybrid Long Short‐Term Memory (LSTM)‐Convolutional Neural Network (CNN) model to forecast weekly oat, corn, soybean and wheat prices in the United States market. The LSTM‐CNN is a multivariate model that uses weather data, macroeconomic data, commodities grain prices and snow factors, including Snow Water Equivalent (SWE), snowfall and snow depth, to make multistep ahead forecasts.ResultsOf all the features, the snow factor is used for the first time for commodity price forecasting. We used the LSTM‐CNN model to evaluate the 5, 10, 15 and 20 weeks ahead forecasting and this hybrid model had the lowest Mean Squared Error (MSE) at 5, 10 and 15 weeks ahead of prediction. In addition, Shapley values were calculated to analyse the feature contribution of the LSTM‐CNN model when forecasting the testing set. Based on the feature contribution, SWE ranked third, fifth and seventh in feature importance in the 5‐week ahead forecast for corn, oats and wheat, respectively, and 7–8 places higher than total precipitation, indicating the potential use of SWE in grain price forecasting.ConclusionThe hybrid multivariate LSTM‐CNN model outperformed other models and the newly involved climate data, SWE, showed the research potential of using snow as an input variable to predict grain prices over a multistep ahead time horizon.

Price determinants of a graded feeder cattle sale

The objective of this study was to deter- mine how numerous factors, including USDA muscle score, affect auction sale prices for feeder cattle in the US South- east. Data came from a monthly feeder cattle sale in Tennessee from 2019 to 2021, and a hedonic pricing model was estimated that considered how average lot weight, lot size, sex, breed, weaning manage- ment, and corn price influence feeder cattle price. The model shows that aver- age lot weight, total head per lot, corn price, lots with weaned cattle, sex, and breed affected the feeder cattle prices as expected based on the literature. A muscle score of 1 increased the sale price feeder cattle by US$9.51 per hundredweight relative to muscle score 2, which is a key finding of this study. This extends the lit- erature by considering the impact of USDA muscle score on feeder cattle prices for cattle in the Southeastern Unit- ed States. This paper also builds on the growing literature examining how various factors affect Southeastern feeder cattle prices. This estimation is useful to help small and medium-sized producers identify optimal management de- cisions to increase the value of their calves. Additionally, this study demonstrates to producers the added value of grades for cattle.

Economic impacts of the Black Sea Grain Initiative

AbstractIn this research, we use econometric analysis to estimate the impacts of the Black Sea Grain Initiative on international wheat and corn prices. Using these results, we assess the economic value of the treaty to the global food system, as well as the regional distribution of the associated welfare savings. We find that the Russian Invasion created economic costs of approximately $116.05 billion in the global wheat and corn markets. In the international wheat market, the Black Sea Grain Initiative reduced prices by 7.9%, offsetting approximately $21.48 billion of these costs. The largest winners from the Initiative outside Ukraine are primarily in the developing world, particularly in the Middle East and North Africa. We do not observe any economically meaningful impacts of the Initiative in the international corn market.

Regression Model of Land Area and Amount of Production to the Selling Price of Corn

Currently, land area, production and maize prices in West Nusa Tenggara province are sometimes unstable. One of the factors affecting the instability of maize prices is the shift in planting patterns at the farm level. The purpose of this study is to determine the effect of land area and total production on the selling price of maize. The method used is quantitative with data analysis techniques using multiple linear regression. The source of data is from the Central Bureau of Statistics, Department of Agriculture and Plantation of NTB. The regression equation found is Y = 3109.911 + 0.007X1 - 0.001X2. This result shows that the X1 variable of 0.007 means that every time there is an increase in the land area variable by 1%, the selling price increases by 7%. While the X2 variable decreased by 1%. The hypothesis with the calculation of the partial t-test of land area is 1.249, which means that land area has no influence on the selling price of NTB corn in 2012-2021. In future research, it is necessary to conduct research on the development of corn planting land area, production, productivity per unit of land area nationally associated with the rate of population growth, corn demand, and the growth of corn imports nationally.

Forecasting the U.S. season-average farm price of corn: Derivation of an alternative futures-based forecasting model

An alternative futures-based procedure is developed to forecast the season-average farm price for U.S. corn, an under-researched price forecast. The new method performs similarly or better than two widely-watched season-average price forecasts, i.e., the World Agricultural Supply and Demand Estimates and the Hoffman futures-based forecasts, at the beginning of the post-harvest season and just as well as those forecasts in most of the other months during the marketing year. We attribute the robust performance of the proposed forecast to its ability to use heterogeneous coefficients for futures and cash prices depending on the underlying market conditions. Improved performance of the proposed forecasts is especially noticeable when the market is more volatile. Overall, the method derived in this study complements the existing forecasts and provides valuable information for decision-makers.

Corn price volatility and producer income protection

O Objective: To estimate the risk indicator of the future price of yellow corn #2 at the Chicago Futures Exchange (USA) regarding the spot price of white corn in the main producing regions in Mexico through the financial volatility indicator. Methodology: The research used the returns of the monthly time series corresponding to the spot price of white corn from January 1998 to December 2020, considering five producer-consumer regions of Mexico and the future price of yellow corn #2 as listed on the Chicago Stock Exchange. To quantify volatility, the generalized autoregressive conditional heteroskedasticity model of order (1,1) was estimated. Results: The yellow corn #2 volatility indicator was 0.9870 (future price). In the case of the spot price of white corn in Mexico, the volatility was 0.7977 for the national price, 0.3385 for the central region, 0.3206 for the western region, and 0.0078 for the southeast region. Implications: The high volatility of yellow corn #2 (close to unity) shows that the international market for this commodity is riskier than the national market or regional markets in Mexico. Conclusions: The national white corn market proved to be riskier than the west, center, and southeast regional markets, which have a higher volatility indicator.

Economics of deficit irrigation utilizing soil moisture probes in the western corn belt

AbstractEffective irrigation management is critical for future food supplies and the prosperity of producers engaged in irrigation production. Through a deficit irrigation field experiment, we determine the financial impact on producers caused by changing irrigation costs, corn prices, extreme weather events, and restricting irrigation levels. Results suggest that the optimal economic strategy within our constrained optimization model is to fully irrigate, with the economic impact highly dependent on commodity prices, restriction level, and irrigation costs. The greatest economic losses caused by irrigation restrictions come from decreases in yield. Some simulations resulted in negative profits, indicating that a switch to alternative crops requiring less irrigation may be warranted.

Publication bias in simulation model studies: The case of ethanol literature.

In this study, we explore the potential for publication bias using market simulation results that estimate the effect of US ethanol expansion on corn prices. We provide a new test of whether the publication process routes market simulation results into one of the following two narratives: food-versus-fuel or greenhouse gas (GHG) emissions. Our research question is whether model results with either high price or large land impact are favored for publication in one body of literature or the other. In other words, a model that generates larger price effects might be more readily published in the food-versus-fuel literature while a model that generates larger land use change and GHG emissions might find a home in the GHG emission literature. We develop a test for publication bias based on matching narrative and normalized price effects from simulated market models. As such, our approach differs from past studies of publication bias that typically focus on statistically estimated parameters. This focus could have broad implications: if in the future more studies assess publication bias of quantitative results that are not statistically estimated parameters, then important inferences about publication bias could be drawn. More specifically, such a body of literature could explore the potential that practices common in either statistical methods or other methods tend to encourage or deter publication bias. Turning back to the present case, our findings in this study do not detect a relationship between food-versus-fuel or GHG narrative orientation and corn price effects. The results are relevant to debates about biofuel impacts and our approach can inform the publication bias literature more generally.

Agricultural policy uncertainty and its impact on commodity markets

AbstractWe construct an agricultural policy uncertainty (APU) index from leading national and local newspapers in major agricultural states in the United States from January 1999 to September 2021. Our analysis shows that economic policy uncertainty Granger causes APU linearly in the short run, but the relationship is nonlinear and bidirectional in the long run. When economic policy uncertainty is considered, APU significantly reduces hedgers' net short positions and speculators' net long positions (except for corn hedgers) in the corn and soybean markets. APU also has a significant positive impact on the log returns of corn and soybean futures prices, consistent with the theory of storage where commodity returns and volatility are positively correlated.

SUPPLY OF CASSAVA IN MAGELANG DISTRICT CENTRAL JAVA PROVINCE

Cassava is a commodity that has the potential to be developed into a regional superior product. Cassava can be processed into other forms of food that are consumed by the community and one of the centers is in Magelang Regency. This study aims to analyze what factors influence the supply of cassava in Magelang Regency. The data used comes from secondary data. The data used from 2008 to 2022 comes from the Central Bureau of Statistics. The results of the study stated that the supply of cassava in Magelang Regency was jointly influenced by the previous year's cassava price, the previous year's sweet potato price, the previous year's cassava production, the harvested area of ​​the previous year's cassava, the previous year's corn price. Meanwhile, partially the supply of cassava in Magelang Regency was influenced by the price of sweet potatoes in the previous year and the cassava harvested area in the previous year. Based on the results of the analysis, the price elasticity of cassava in the previous year on cassava supply in Magelang Regency was inelastic for both short-term elasticity (0.701) and long-term elasticity (0.02).

Simulated Ecosystem and Farm-Level Economic Impacts of Conservation Tillage in a Northeastern Iowa County

While the ecological benefits of no-till are largely indisputable, the economic impacts are less certain, and the latter may be partly to blame for lower-than-expected adoption of no-till. In this study, we contribute to a better understanding of the ecosystem and farm-level economic impacts of no-till, with Buchanan County in the northeastern region of the U.S. State of Iowa as the backdrop due to previously established data and model validation efforts in that region. Using the Agricultural Policy Environmental eXtender (APEX) and Farm Economic Model (FEM), we simulated two tillage scenarios—a conservation tillage baseline and no-till—for continuous corn and corn–soybean rotations in Buchanan County using gridded historical climate data. We find that no-till provides clear ecosystem benefits, except that soluble nutrient losses might actually rise. We also find that under current commodity prices for corn and soybeans, no-till is not as profitable as the conservation tillage baseline. For no-till to be at least as profitable as the baseline under current commodity prices, the yield penalty associated with no-till cannot be higher than 1.5% for corn and 0.8% for soybeans, or similar combinations that entail a revenue penalty of about $24,000 for an 809-hectare continuous corn or corn–soybean operation. Given the simulated yield penalties associated with no-till, corn and soybean prices would have to be substantially lower in order for no-till to break even. Consequently, incentives for conservation practice implementation may need to be tied to commodity prices and yield penalties in order to elicit greater adoption rates.

US biofuel market persistence and mean reversion properties

This paper investigates the market persistence and mean reversion properties for corn, bioethanol and gasoline prices in the US biofuel industry, evaluating long memory effects with fractional integration techniques from January 1982 to May 2022 with USDA data. Empirical results show evidence of no mean reversion properties for the prices in the three series though some support of it is found when the differences of bioethanol and gasoline are taken with respect to corn. Thus, external shocks in the original series are expected to remain persistent and would require additional policy measures to recover the original trend. Furthermore, the impact of Covid on the time series has been analyzed by comparing the scenarios pre and post pandemic, finding evidence of no major changes in the integration orders in all the series under analysis.

Betting on Black Gold: Oil Speculation and U.S. Inflation (2020–2022)

Sharp increases in systemically important crude oil prices have been a major cause of the recent surge in the inflation rate in the U.S. This paper investigates the extent to which the increase in oil prices can be attributed to excessive speculation in the oil futures market. Our analysis suggests that excessive speculation in the crude oil market has been responsible for 24%–48% of the increase in the WTI crude oil price during October 2020–June 2022. These estimates translate into an oil price increase of around $18-$36 per barrel and an increase in the U.S. PCE inflation rate by circa 0.75–1.5% points during the same period. We complement the analysis with an empirical investigation of the crude oil market, which shows that (speculative) long noncommercial open-interest positions in oil futures have increased considerably relative to short noncommercial positions. We further find that higher futures prices for crude oil “Granger-cause” oil spot prices, the futures prices of corn and soybeans and the fertilizer price. These econometric results show that oil speculators have to be held accountable for not just raising oil prices, but also driving up food commodity prices. We finally discuss measures to clamp down on excessive speculation in oil in order to eliminate its systemically adverse consequences for the U.S. economy.

Agricultural Commodities in the Context of the Russia-Ukraine War: Evidence from Corn, Wheat, Barley, and Sunflower Oil

The Russian invasion of Ukraine on 24 February 2022 accelerated agricultural commodity prices and raised food insecurities worldwide. Ukraine and Russia are the leading global suppliers of wheat, corn, barley and sunflower oil. For this purpose, we investigated the relationship among these four agricultural commodities and, at the same time, predicted their future performance. The series covers the period from 1 January 1990 to 1 August 2022, based on monthly frequencies. The VAR impulse response function, variance decomposition, Granger Causality Test and vector error correction model were used to analyze relationships between variables. The results indicate that corn prices are an integral part of price changes in wheat, barley and sunflower oil. Wheat prices are also essential but with a weaker influence than that of corn. The additional purpose of this study was to forecast their price changes ten months ahead. The Vector Autoregressive (VAR) and Vector Error Correction (VECM) fanchart estimates an average price decline in corn, wheat, barley and sunflower oil in the range of 10%. From a policy perspective, the findings provide reliable signals for countries exposed to food insecurities and inflationary risk. Recognizing the limitations that predictions maintain, the results provide modest signals for relevant agencies, international regulatory authorities, retailers and low-income countries. Moreover, stakeholders can become informed about their price behavior and the causal relationship they hold with each other.

Optimisation of crop rotations: A case study for corn growing practices in forest-steppe of Ukraine

The formation of optimal crop rotations is virtually unsolvable from the standpoint of the classical methodology of experimental research. Here, we deal with a mathematical model based on expert estimates of “predecessor-crop” pairs’ efficiency created for the conditions of irrigation in the forest-steppe of Ukraine. Solving the problem of incorporating uncertainty assessments into this model, we present new models of crop rotations’ economic efficiency taking into account irrigation, application of fertilisers, and the negative environmental effect of nitrogen fertilisers’ introduction into the soil. For the considered models we pose an optimisation problem and present an algorithm for its solution that combines a gradient method and a genetic algorithm. Using the proposed mathematical tools, for several possible scenarios of water, fertilisers, and purchase price variability, the efficiency of growing corn as a monoculture in Ukraine is simulated. The proposed models show a reduction of the profitability of such a practice when the purchase price of corn decreases below 0.81 EUR∙kg –1 and the price of irrigation water increases above 0.32 EUR∙m –3 and propose more flexible crop rotations. Mathematical tools developed in the paper can form a basis for the creation of decision support systems that recommend optimal crop rotation variations to farmers and help to achieve sustainable, profitable, and ecologically safe agricultural production. However, future works on the actualisation of the values of its parameters need to be performed to increase the accuracy.

Do agricultural prices respond to interest on reserves?

This paper elucidates the responsiveness of agricultural commodity prices to changes in interest on reserves, an unconventional monetary policy. It uses a vector autoregression model on monthly data for the United States, from October 2008 to July 2019. The empirical findings show that an increase in the interest rate on reserves leads to a drop in the price of corn, sorghum, and cotton. An increase in the interest on reserves declines the money supply, which increases the real interest rate and makes bonds lucrative over alternative investments such as agricultural commodities. This depresses the farm commodity prices, and the effect reaches its maximum after nearly four months. However, the impact on wheat prices is relatively weak. The estimations are robust to alternate specifications. The results of this study confirm the strong linkage between money supply and the prices of agricultural commodities. Food security for those nations that are heavily dependent on imported food grain from the U.S. market are dependent on the U.S. monetary policy.

When cointegration is interrupted: Price transmission analysis in the Italian dairy‐feed industry

AbstractWe analyze price transmission dynamics in the under‐investigated feed supply chain, applying a special case of the Threshold Vector Error Correction model where the cointegrating relationship is interrupted. Weekly Italian dairy feed and corn prices are used, finding evidence of asymmetric price transmission when the feed industry's margins are squeezed and highly volatile. The proposed Interrupted Threshold Cointegration model represents a valid complement to existing threshold and regime‐switching techniques, allowing for the application of an agile nonstructural error correction model when standard cointegration tests fail to detect any long‐run relationship between price series. [EconLit Citations: Q11, Q13, C24].

Policy reform and farmers' coping strategies: The case of corn price shocks in Heilongjiang Province of China

AbstractA substantial decrease in corn prices caused by the policy reform in 2016 in China led to heterogeneous supply responses of corn producers: decreasing corn acreage, making no change in corn acreage, and expanding corn acreage. We investigate the causes and consequences of cropland allocation of the supply responses using the balanced panel data on corn‐producing households from 2015 through 2016 in Heilongjiang Province. We find that the corn producers largely acted in accordance with their own household resource endowments to make their choice decisions in response to the corn price shocks. Our results show that the decrease in corn prices led to a small decline in total corn acreage and a considerable increase in total acreage for soybeans and rice in 2016 and that the increase in total soybean acreage was more through area expansion rather than through substitution. Our results also show that crop structure for large‐scale producers was more responsive to corn price than for medium‐ and small‐scale producers. Given that continuous corn rotation prevails currently in Heilongjiang, our major finding suggests that the acreages planted to corn and soybeans cannot be easily adjusted by the market.

Eksistensi Tengkulak dalam Menunjang Perekonomian Petani Jagung di Desa Juriya, Kecamatan Bilato, Kabupaten Gorontalo

The problems faced by corn farmers in Juriya Village are the same as those of farmers in general, namely problems with capital, marketing, inadequate facilities and unstable market prices, so that farmers really need help from middlemen who provide all the needs of farmers. This research is to find out how the existence of middlemen in supporting the economy of corn farmers in Juriya Village, Bilato District, Gorontalo Regency. The method used in this research is a qualitative descriptive approach. Data collection techniques in this study using observation techniques, interviews and documentation. The results showed that: First, the existence of middlemen in Juiya Village, Bilato District, Gorontalo Regency is very helpful to the farmer’s economy such as needs, both in terms of primary needs and secondary needs so that the farmers always depend on middlemen or bosses for their lives. Second, the factors that cause farmers to depend on middlemen in Juriya Village include lack of capital, fulfillment of needs, cooperation, trust, and inadequate facilities. Third, both middlemen and corn farmers have their own ways of maintaining their working relationship. Fourth, the effects of a farmer’s dependence on middlemen include a rise in dependency, exploitation of corn prices by middlemen, and inequality.

Pooled analysis on the effects of inclusion, moisture, and oil removal from distillers grains on cattle performance and economic returns in diets with different corn processing.

A synthesis of data including over 9,300 head of cattle and over 980 pen means was conducted to evaluate the effect of corn processing method, distillers grains type, oil removal from distillers, and dietary distillers concentration on cattle performance and total feeding costs under different historical price scenarios. The corn processing method was either steam-flaked corn, high-moisture or dry-rolled corn or a blend of the latter two and their effects on performance with and without distillers grains. Wet, modified, and dry distillers were analyzed as either full fat or de-oiled products in diets at various dietary concentrations with either corn type to determine cattle performance. Performance estimates were used to determine the inclusion level that minimized total finishing costs ($/head) for each diet given various assumptions on corn price, corn-to-distillers price ratio, supplemental protein cost, and steam flake processing cost. Feeding distillers grains in steam-flaked or high-moisture and dry-rolled corn diets resulted in improved feed conversion and reduced cattle feeding costs, including lower supplemental protein costs and lower total feeding costs compared with a diet without distillers. However, feeding diets with steam-flaked corn improved feed conversion regardless of distillers type or level of inclusion. Feeding full fat distillers improved feed conversion relative to de-oiled distillers. Cattle fed de-oiled distillers were more efficient than those fed diets containing no distillers grains. At all corn price and corn-to-distillers price ratios examined, feeding some level of distillers of any type produced a lower total feeding costs relative to a diet without distillers. Different robustness tests were conducted to determine the impact of varying steam flake corn processing costs and the cost of supplemental protein on the optimal level of distillers inclusion level. The break-even cost a producer could pay for steam-flaking corn varied but was significantly higher when corn price was high. Feeding distillers grains in steam-flaked or high-moisture and dry-rolled corn diets resulted in improved feed efficiency, reduced cattle feeding costs, including replacement of other supplemental protein and associated costs compared to a diet without distillers grains.