Agribusiness: Understanding Climatic Dependence & Seasonality - Essay Sample

Introduction

Agricultural production has specificities due to the strong dependence on climatic factors, light, and temperature. According to Borodin et al. (2016), perishability, homogeneity, seasonality of supply, climatic risks, low elasticity of demand, and rigidity of supply are intrinsic characteristics of agricultural production and its products. Seasonality is a direct reflection of climatic dependence that limits production in specific periods, as well as all segments of the agribusiness. These factors make the productions present both economic and cultural limitations and unfeasibility in time of the year, affecting production costs, and generating an increase in prices due to the low quantity offered which is reflected in input prices (Borodin et al., 2016). This fact is easily noticeable when the terms crop and off-season are observed in the agricultural environment.

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Strategic planning must design actions that take into account the product category, its characteristics, and also factors such as seasonality. Advances in production technologies, in the fields of irrigation, drainage, greenhouses, and appearance of tolerant or resistant varieties to certain phenomena (translated into changes in the respective production functions), can increase the farmer’s control about some of the climate and soil variables, thus allowing to reduce some of this seasonality (Kamble et al., 2020). This market concentration eases the resellers and the industry itself from the seasonal effects of production in relation to the demand for its products and its profitability.

Non-Centralized Distribution Infrastructure

Non-centralized distribution makes the supply chain processes costly, especially when considering the share of transporting commodities per unit cost of a particular product. A restructuring of the distribution system in the profile of these companies can alleviate this problem since the entire context of the productive chain in which they are inserted can be analyzed and known. Centralized distribution infrastructure analyzes the regional realities and location of the resale of agricultural inputs and, thus, can guide businesspeople in the industry, allowing them to know the behavior of internal and external factors of the agricultural production and knowing how they perform in practice.

Centralized distribution infrastructure aims at building paths that enable better integration between the different links that make up a production chain (Davis & Goldberg, 1957; Goldberg, 1968; Lauret, 1978; Zylbersztajn, 1995; Zylbersztajn, 2000). This set of factors - long distances, dependence on road transport, and lack of infrastructure - increases the value of freight, significantly reduces transport productivity, and implies higher costs in agribusiness logistics.

It is evident that the development and expansion of other modes of transport would be positive for the sector. Railway and waterway networks should be the target of major investments since they have lower costs, have greater capacity, and are less polluting. The integration between road, rail, and waterway networks, called “multimodality”, can bring even greater cost reductions compared to the unimodal situation.

Lack of Coordination and Integration of the Logistics Network

The efficiency of modern agribusiness relies more on the structural aspects of the production chain, insofar as it depends on the capacity to respond to the evolution of demand conveyed by large distribution and requires new upstream integration forms, to ensure the flow and quality of the raw material (Behzadi et al., 2018). Coordination and integration allow to analyze the market around them, in order to outline actions and measures that go, when necessary, against the changes involved, thus making it possible to devise strategies to get around the possible billing falls that are linked more to the internal issues (seasonality of production, climatic factors, and market behavior) (Li et al., 2020). Improving management and optimizing logistics in agribusiness are fundamental measures to maintain and expand production in the field. Therefore, a new approach is envisaged, with competition occurring much more between chains, where it is no longer possible to plan sectorally, without taking into account the developments along the production chain, especially in the case of Agribusiness, highly dependent on more diverse market and legal requirements (environmental and health).

This new paradigm assumes that it is a question of systemic competitiveness, and it is essential that mechanisms be sought for the formation of value chains, incorporating knowledge in all forms of interaction between the components of the chain. In this way, the chain needs mechanisms through which to systematically seek to know and map: 1) trends for product differentiation, with explicit strategies; 2) institutional controls and 3) control of the various suppliers, that is, it is essential to create the best conditions to optimize the governance of the chain as a whole and, in particular, in the case of agribusiness, of the supply chains (Behzadi et al., 2018).

Technology is fundamental to increase production through increased productivity and to manage all the processes involved in the production of a food product, regardless of the scale of production (Kamble et al., 2020). Some software, such as production sensors and automated fleet supply systems also allow for savings in resources, enabling greater control of operating costs. This is possible because, through the use of these solutions, it is possible, respectively, to rationalize the application of inputs and to control each drop of fuel used by the machinery. In addition to assisting in business administration through integrated management systems (ERP), for example, digital agriculture tools help in monitoring all stages of the production chain.

References

Behzadi, G., O’Sullivan, M. J., Olsen, T. L., & Zhang, A. (2018). Agribusiness supply chain risk management: A review of quantitative decision models. Omega, 79, 21-42.

Borodin, V., Bourtembourg, J., Hnaien, F., & Labadie, N. (2016). Handling uncertainty in agricultural supply chain management: A state of the art. European Journal of Operational Research, 254(2), 348-359. https://doi.org/10.1016/j.ejor.2016.03.057

Kamble, S. S., Gunasekaran, A., & Gawankar, S. A. (2020). Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179-194. http://agri.ckcest.cn/file1/M00/0F/A7/Csgk0F4oCMeAbz3xABM8_eyy10o901.pdf

Li, H., Li, D., & Jiang, D. (2020). Optimising the configuration of food supply chains. International Journal of Production Research, 1-25. https://doi.org/10.1080/00207543.2020.1751337