Introduction

In Mexico, poultry farming is the most dynamic livestock production activity. Production and consumption are growing steadily, production and distribution systems are more integrated than in other livestock sectors¹, and it is the principal means of transforming vegetable protein into animal protein². Chicken meat is clearly the preferred animal protein source among Mexican consumers³.

Domestic chicken meat production grew from 1.26 million tons in 1996 to 3.07 million tons in 2016, a 4.55 % average annual growth rate (AAGR). During this period, apparent national consumption increased from 1.39 to 3.84 million tons, at a 5.21 % average annual increase. Indeed, growth in consumption outstripped that in production. The difference between them was covered by imports, which increased by a 7.66 % annual average and, during this period, represented approximately 18 % of chicken meat consumption³.

This dynamism in poultry production has exhibited disparities between regions over time. For example, in 2016 producers in Mexico’s Central-West (CW) and Central-Eastern (CE) regions achieved strong economic growth, contributing a cumulative 53.37 % to national production, whereas, even when taken together, the Northeast (NE) and Yucatan Peninsula (PE) regions contributed only 7.76 %⁴.

During this period, the price of chicken carcasses varied between regions. For example, in 2016 the price per kilogram ($/kg) in the CW region, that with the highest chicken meat production, was $30.53 pesos per kilogram, which was 3.38% lower than the previous year. This decline in production costs responded to improvements in production conditions, control of avian influenza, and decreases in the international prices of the main fodder grains⁵. During the same period prices varied from $29.68 / kg in the CE region, to $32.22 / kg in the PE region and $33.37 / kg in the East (ET)⁴.

Sorghum, the principal component of chicken feed⁶, also varied widely in price between regions during 2016. For example, compared to 2015 the price of sorghum increased by 10.13 % in the NE, 8.41 % in the North (NT) and 2.84 % in the CE, but decreased by 2.84 % in the PE⁴.

Demand by region varied from 1996 to 2016, from 5.15 % AAGR in the CE to 4.95 % in the CW and 4.98 % in the South (ST). In part this behavior can be explained by increases in per capita gross domestic product (GDP) (2.67 % in CE; 4.35 % in CW; and 3.27 % in ST)⁷, population growth (1.17 % in CE; 0.99 % in CW; and 1.02 % in ST)⁸, consumer preference, number of household members, and income. All these factors positively affect meat consumption probability⁹.

Interregional differences in the dynamics of the different economic and technological variables that determine chicken meat supply and demand clearly exist. Econometric models are therefore needed to represent how different regional markets operate, and to generate tools that help guide public policy makers and provide alternatives for designing production support programs based on regional needs.

Chicken meat supply and demand is apparently affected by regional variation in the variables that influence it. The present study objective was to quantify the effect of the main economic and technological variables that influence chicken meat supply and demand in eight regions in Mexico (Northwest, North, Northeast, Central West, Central East, South, East and Yucatan Peninsula) from 1996 to 2016.

Material and methods

Regionalization is a methodology, procedure or intervention applied to reorganize a country into smaller territorial units with common characteristics. It is a basic methodological tool in environmental planning since it provides knowledge of regional resources for appropriate management¹⁰. Using this approach Mexico has been divided into eight economic regions to analyze chicken meat supply and demand at the regional level¹¹ (Table 1).

A multiple linear regression econometric model was developed for chicken carcass supply and demand in each of the regions from 1996 to 2016. Supply variables included: the price of chicken meat; technology (measured as feed efficiency); and input costs (i.e. feed price)¹². Demand variables were the price of chicken meat; financial income; population; and the prices of substitute or additive products¹³. The models represent each regional market’s internal behavior.

The data for each variable was obtained from sources such as the Agrifood and Fisheries Information Service (Servicio de Información Agroalimentaria y Pesquera - SIAP), the Agricultural Trusts (Fideicomisos Instituidos en Relación con la Agricultura -FIRA), the National Institute of Statistics, Geography and Data Processing (Instituto Nacional de Estadística, Geografía e Informática - INEGI), the National Council on Population (Consejo Nacional de Población - CONAPO) and the National Market Data and Integration System (Sistema Nacional de Información e Integración de Mercados - SNIIM). Feed efficiency (FE) was taken from previous reports for 1996 and 2016 ¹⁴, and data for the intervening years estimated with the annual average growth formula: r = (D_f / D_i)^1/n - 1; where D_f is final FE data, D_i is initial FE data, and r is average annual growth rate (AAGR).

Parameters in the linear models associated with the supply and demand function were estimated with the ordinary least squares (OLS) method¹⁵. This helped to identify the effect of each of the independent variables on the dependent variable, as well as generate the best unbiased linear and minimum variance estimators. These analyses were run with the SAS statistical package¹⁶.

Statistical congruence of the supply and demand models was determined with the coefficient of determination (R²). Calculation of the statistical significance of each equation was done with the F test, and that for the individual significance of each coefficient with the Student t test. Economic evaluation was done considering the signs and magnitude of the coefficients of the variables of the supply and demand functions. These were interpreted using the fundamentals of economic theory. In other words, the relationship between chicken carcass supply and price with feed efficiency must be direct, whereas that with the price of pork and feed must be inverse. The relationship between chicken carcass demand and current price must be inverse, while it must be direct with respect to per capita GDP, current price of beef and human population.

In some regions, the variables of chicken meat price, pork (alternative good) price and feed price lagged one to two years behind other regions. Producers in these regions did not immediately decrease production in response to changing prices. Other factors also contributed to this lag such as interregional differences in length of the animal production cycle, degree of investment, production volume and company financial situation.

Calculations were done of the economic elasticities of each explanatory variable affecting chicken meat supply and demand in each region. These were evaluated based on the sign and magnitude of their coefficients, and interpreted following economic theory.

Five econometric models were proposed to calculate chicken meat supply and demand in the eight regional markets:

CMS_t = β₁₁ + β₁₂ CMRP_t + β₁₃ PRP_t-2 + β₁₄ RFP_t + β₁₅ FE_{t +} £_t (NW and NT)

CMS_t = β₂₁ + β₂₂ CMRP_t + β₂₃ PRP_t + β₂₄ RFP_t + β₂₅ FE_{t +} £_t (NE, CW and CE)

CMS_t = β₃₁ + β₃₂ CMRP_t-1 + β₃₃ PRP_{t -2} + β₃₄ RFP_t + β₃₅ FE_{t +} £_t (ST and ET)

CMS_t = β₄₁ + β₄₂ CMRP_t + β₄₃ PRP_t-2 + β₄₄ RFP_t-2 + β₄₅ FE_{t +} £_t (PE)

CMD_t = β₅₁ + β₅₂ CMRP_t + β₅₃ RGDP_t + β₅₄ BRP_t + β₅₅ POP_{t +} £_t

Where:

CMS_t): chicken meat (carcass) supply in current period, estimated based on regional chicken carcass production (t);

CMRP_t: average real weighted regional price of chicken carcass, in current period ($/kg);

CMRP_t-1: average real weighted regional price of chicken carcass, with a one-year lag ($/kg);

PRP_t: average real weighted regional price of pork, in current period ($/kg);

PRP_t-2: average real weighted regional price of pork, with a two-year lag, as alternative product ($/kg);

RFP_t: average real weighted regional price of chicken feed, in current period, estimated based on price of sorghum as main ingredient ($/kg);

RFP_t-2: average real weighted regional price of chicken feed, with a two-year lag, estimated based on price of sorghum as main ingredient ($/kg);

FE_t: feed efficiency;

CMD_t: volume of chicken carcass demand, in current period, estimated based on apparent regional consumption (thousands of tons);

RGDP_t: real regional per capita gross domestic product, current period (thousands of $/person), as a variable for approximating national per capita available income;

BRP_t: average real weighted regional price of beef ($/kg), as substitute product;

POP_t: regional population, current period (millions of inhabitants/region).

All monetary variables were deflated based on the National Consumer Price Index (Índice Nacional del Precio al Consumidor - INPC; 2012 baseline = 100).

Model formulation was based on economic theory and empirical evidence. Chicken meat producers in Mexico base decisions on increasing, maintaining or decreasing production, on the price of chicken, the prices of the inputs needed to produce it and alternative products such as pork^17,18.

Feed efficiency (FE) was used to reflect technological progress in chicken meat production volume since it is one of the variables that most influence production in the poultry sector. It is also a factor that has stimulated increased chicken meat production in different regions of Mexico through genetic selection to produce chickens that generate more meat with the same amount of feed. Poultry farmers can thus continue to supply their product supported by increased productivity⁶.

The FE variable integrates technological advances and helps to explain why poultry farmers continue to supply their product in the market, despite a clear downward trend in the price of chicken meat and price increases in sorghum, the main feed input⁶. The price of pork as an alternative good was included because some companies produce both chicken and pork^19,20,21, using the same feed inputs^17,22.

Chicken meat and pork prices were calculated using the average real weighted regional price of the product in carcass form. Feed price was considered to be the price of sorghum since it is the main ingredient in both chicken and pig feed⁶. All prices were calculated from the weighted average of all the states within each of the eight regions.

Regional chicken meat demand was calculated by considering apparent regional consumption as a variable approximating regional demand. Regional consumption was estimated based on production, plus imports and minus exports, within each region. The result was then multiplied by each region’s population in a given year of the analyzed time series.

Based on economic theory, demand determinants included in the model included average real weighted regional price of chicken carcass [CMRP_t]); income (real regional per capita gross domestic product [RGDP_t]); price of substitute good (average weighted real regional price of beef [BRP_t]) and regional population (POP_t). All variables were calculated in the current period^12,13.

Elasticity values for each explanatory variable by region were calculated by multiplying the coefficients of the partial derivatives of the regional equations by the final observed value of each independent variable given the quantities for supply and demand. Since the supply and demand linear functions contain variable elasticity throughout their range of estimation, elasticity was calculated for the final year of the analyzed period, which is closest to the present²³. The effects established in the functional relationships were quantified in this way.

Results and discussion

In most of the regions the chicken carcass supply equations exhibited a high coefficient of determination, the highest value being in the CW (R² = 98) and the lowest in the NE (R² = 65) (Tables 2 and 3). In the demand models, the regional coefficients of determination ranged from 0.98 to 0.99. The NW, NT, NE, CE and PE regions exhibited the best fit to the data. The supply and demand model was significant (P<0.05) according to the Fisher’s F test.

The contribution of each of the explanatory variables in both models was evaluated according to their asymptotic t or t-ratio. This must be greater than the unit since this indicates that the estimated parameter’s value is greater than its standard error²⁴. Not all the supply variables were significant in all the regions when using this parameter. The coefficient of the feed efficiency variables was significant in most regions, except the CE and PE. However, the coefficient of the chicken meat price variables was significant only in the NE, CW, CE and PE, but not in the NW, NT, ST and ET. Likewise, the coefficient of the pork price variable was significant in the NE, CE, ET and PE, but not in the NW, NT, CW and ST. In the demand models, the coefficients for chicken meat price, per capita GDP and population were significant (P<0.05) for all eight regions. In contrast, the beef price variable was significant only in the NW and NE, but not in the remaining six regions. Using Klein’s practical rule²⁵, the present results indicate there to be no multicollinearity between the explanatory variables in the regional supply and demand models.

Conclusions and implications

Technological progress (defined as feed efficiency) was the factor that most influenced growth in poultry production in most of the regions in Mexico. Regional demand for chicken meat was elastic in relation to population growth; that is, in all the regions increases in population had the largest influence on increases in chicken meat consumption. The present results explain to what extent the evaluated explanatory variables affect regional supply and demand of chicken meat in Mexico.