A Fully Calibrated Generalized CES Programming Model of Agricultural Supply

The use of prior information on supply elasticities to calibrate programming models of agricultural supply has been advocated repeatedly in the recent literature (Heckelei and Britz 2005). Yet, Mérel and Bucaram (2009) have shown that the dual goal of calibrating such models to a reference allocation while replicating an exogenous set of supply elasticities is not always feasible. This article lays out the methodological foundation to exactly calibrate programming models of agricultural supply using generalized CES production functions. We formally derive the necessary and sufficient conditions under which such models can be calibrated to replicate the reference allocation while displaying crop-specific supply responses that are consistent with prior information. When it exists, the solution to the exact calibration problem is unique. From a microeconomic perspective, the generalized CES model is preferable to quadratic models that have been used extensively in policy analysis since the publication of Howitt’s (1995) Positive Mathematical Programming. The two types of specifications are also compared on the basis of their flexibility towards calibration, and it is shown that, provided myopic calibration is feasible, the generalized CES model can calibrate larger sets of supply elasticities than its quadratic counterpart. Our calibration criterion has relevance both for calibrated positive mathematical programming models and for “well-posed” models estimated through generalized maximum entropy following Heckelei and Wolff (2003), where it is deemed appropriate to include prior information regarding the value of own-price supply elasticities.Positive mathematical programming, generalized CES, supply elasticities, Crop Production/Industries, Production Economics,

Ethanol Plant Investment in Canada: A Structural Model

Most of the fuel ethanol plants in Canada were built recently and either use corn or wheat as feedstock. It is important to determine what factors affect decisions about when and where to invest in building new ethanol plants and which feedstock is chosen as feedstock. In this paper we model the decision to invest in ethanol plants using a structural model of a dynamic game. We find that competition between plants is enough to deter local investments, the availability of feedstock is important in determining plant location, and the effects of policy support for wheat-based plants are significant

Stage-specific, Nonlinear Surface Ozone Damage to Rice Production in China.

China is one of the most heavily polluted nations and is also the largest agricultural producer. There are relatively few studies measuring the effects of pollution on crop yields in China, and most are based on experiments or simulation methods. We use observational data to study the impact of increased air pollution (surface ozone) on rice yields in Southeast China. We examine nonlinearities in the relationship between rice yields and ozone concentrations and find that an additional day with a maximum ozone concentration greater than 120 ppb is associated with a yield loss of 1.12% ± 0.83% relative to a day with maximum ozone concentration less than 60 ppb. We find that increases in mean ozone concentrations, SUM60, and AOT40 during panicle formation are associated with statistically significant yield losses, whereas such increases before and after panicle formation are not. We conclude that heightened surface ozone levels will potentially lead to reductions in rice yields that are large enough to have implications for the global rice market

Ethanol plant investment in Canada: A structural model 1

Most of the fuel ethanol plants in Canada were built recently and either use corn or wheat as feedstock. It is important to determine what factors affect decisions about when and where to invest in building new ethanol plants and which feedstock is chosen as feedstock. In this paper we model the decision to invest in ethanol plants using a structural model of a dynamic game. We find that competition between plants is enough to deter local investments, the availability of feedstock is important in determining plant location, and the effects of policy support for wheatbased plants are significant

Environmental Policies in the Transportation Sector: Taxes, Subsidies, Mandates, Restrictions, and Investment

The transportation sector is associated with many negative externalities, including air pollution, global climate change, and traffic congestion. In this paper we discuss several possible policies for addressing the emissions and other environmental externalities from the transportation sector, including taxes, subsidies, mandates, restrictions, and investment. Most economists generally recommend that policy-makers use incentive- (or market-) based instruments as opposed to command and control policies whenever possible. However, various economic and political constraints can preclude policy instruments that would in theory achieve a first-best outcome from being employed, and policy-makers have often implemented alternative policies such as subsidies, mandates, restrictions, and/or investment instead. Our discussion and analysis of these policies draws upon and synthesizes research using theoretical models, behavioral and experimental economics, empirical analyses, and structural econometric modeling

What Factors Affect the Decision to Invest in a Fuel Ethanol Plant?: A Structural Model of the Ethanol Investment Timing Game

DTRT13-G-UTC29Citation: Yi, Fujin, and C.-Y. Cynthia Lin Lawell. (Draft 2017). What Factors Affect the Decision to Invest in a Fuel Ethanol Plant?: A Structural Model of the Ethanol Investment Timing Game.This paper analyzes how economic factors, strategic factors, and government policies affect the decision to invest in building new ethanol plants in Europe

结合轻量级麦穗检测模型和离线Android软件开发的田间小麦测产

The number of spikes per unit area is a key yield component for cereal crops such as wheat, which is popularly used in wheat research for crop improvement. With the fast maturity of smartphone imaging hardware and recent advances in image processing and lightweight deep learning techniques, it is possible to acquire high-resolution images using a smartphone camera, followed by the analysis of wheat spikes per unit area through pre-trained artificial intelligence algorithms. Then, by combining detected spike number with variety-based spikelet number and grain weight, it is feasible to carry out a near real-time estimation of yield potential for a given wheat variety in the field. This AI-driven approach becomes more powerful when a range of varieties are included in the training datasets, enabling an effective and valuable approach for yield-related studies in breeding, cultivation, and agricultural production. In this study, we present a novel smartphone-based software application that combines smartphone imaging, lightweight and embedded deep learning, with yield prediction algorithms and applied the software to wheat cultivation experiments. This open-source Android application is called YieldQuant-Mobile (YQ-M), which was developed to measure a key yield trait (i.e. spikes per unit area) and then estimate yield based on the trait. Through YQ-M and smartphones, we standardized the in-field imaging of wheat plots, streamlined the detection of spikes per unit area and the prediction of yield, without a prerequisite of in-field WiFi or mobile network. In this article, we introduce the YQ-M in detail, including: 1) the data acquisition designed to standardize the collection of wheat images from an overhead perspective using Android smartphones; 2) the data pre-processing of the acquired image to reduce the computational time for image analysis; 3) the extraction of wheat spike features through deep learning (i.e. YOLOV4) and transfer learning; 4) the application of TensorFlow.lite to transform the trained model into a lightweight MobileNetV2-YOLOV4 model, so that wheat spike detection can be operated on an Android smartphone; 5) finally, the establishment of a mobile phone database to incorporate historic datasets of key yield components collected from different wheat varieties into YQ-M using Android SDK and SQLite. Additionally, to ensure that our work could reach the broader research community, we developed a Graphical User Interface (GUI) for YQ-M, which contains: 1) the spike detection module that identifies the number of wheat spikes from a smartphone image; 2) the yield prediction module that invokes near real-time yield prediction using detected spike numbers and related parameters such as wheat varieties, place of production, accumulated temperature, and unit area. During our research, we have tested YQ-M with 80 representative varieties (240 one-square-meter plots, three replicates) selected from the main wheat producing areas in China. The computed accuracy, recall, average accuracy, and F1-score for the learning model are 84.43%, 91.05%, 91.96%, and 0.88, respectively. The coefficient of determination between YQ-M predicted yield values and post-harvest manual yield measurement is 0.839 (n=80 varieties, P<0.05; Root Mean Square Error=17.641 g/m2). The results suggest that YQ-M presented here has a high accuracy in the detection of wheat spikes per unit area and can produce a consistent yield prediction for the selected wheat varieties under complex field conditions. Furthermore, YQ-M can be easily accessed and expanded to incorporate new varieties and crop species, indicating the usability and extendibility of the software application. Hence, we believe that YQ-M is likely to provide a step change in our abilities to analyze yield-related components for different wheat varieties, a low-cost, accessible, and reliable approach that can contribute to smart breeding, cultivation and, potentially, agricultural production

Grain Subsidy, Liquidity Constraints and Food security—Impact of the Grain Subsidy Program on the Grain-Sown Areas in China

This study examined the effects of China’s grain subsidy program, the largest food self-sufficiency project in the developing countries, on grain-sown areas within a context of liquidity constraints. A large household level panel was used to evaluate how the subsidy affects the cultivation schedule of farm households through the relaxation of households’ liquidity con¬straints. Results suggest that in general, the grain subsidy program significantly improved farm households’ grain planting areas for liquidity-constrained households. This finding provides a more comprehensive understanding of the effects of China’s grain subsidy than previous studie

Cash transfers and multiplier effect: lessons from the grain subsidy program in China

This study examines the multiplier effects of the grain subsidy program in China, which is a large food self-sufficiency project that is implemented as a cash transfer program. Income multiplier effects have not been examined in the evaluation of the grain subsidy program although increasing the income of farmers is the original goal of this project. A large number of household-level observations are employed to measure the program’s income multiplier. Results show that the grain subsidy program has an unrealized high income multiplier, and the income promotion effect of the transferred subsidies is from agricultural production derived by intensifying various input uses for each unit of land. The multiplier effect can be particularly utilized by households with good education and poor farmers in less developed regions. Hence, to maximize the income multiplier effect, the grain subsidy distribution method should consider these criteria instead of retaining the prevalent standard that is based on contracted land areas