4.1. Considerations Regarding Filtering
Implementing forecasting requires large datasets. Thus, when analyzing old databases, it can be challenging to discover what products have been available and have enough data for model computation [19
]. For the data collection in this work, the only available data source was SNIIM’s website. SNIIM is an initiative of the Mexican government. This initiative was undertaken by the Secretary of the Economy and involves daily data collection from the major wholesale markets in Mexico (45 throughout the country). Interviewers collect the data by visiting these wholesale markets every day and gathering information directly from the sellers. Thus, the data gathered represent the actual market. The data are then sorted and arranged by price from lowest to highest. The data also include the origin of the product, which provides information about the product flow and identifies the producer.
The characterization of the data was carried out to understand the data and their dynamics over time. We found that only some products had enough data for price forecasting.
While persistent products can be found in local markets all year long, seasonal products, as their name suggests, can only be found during specific months. Although it is possible to forecast the price for both kinds of products, it is only valuable to predict the prices of the former because seasonal products are only sold during their weeks with a commercialization opportunity. Moreover, new products may become persistent over time, but it is not clear whether random, ghostly, and dead products will ever become persistent in the future.
Family grouping organizes data hierarchically so a comparative analysis can be performed to recognize related products. In the multivalent family, product diversification was observed when two different products from the same family were commercialized in the same period of time, while the mixed family resulted in changes where one product was replaced by another product. Sometimes this replacement implies an improvement in its quality. For example, lime #3 was replaced with lime # 5, which implies an increase in its size from 34–37 mm to more than 39 mm, and according to the Mexican normative, the size of the lime is an attribute of quality [20
4.2. Modeling for Decision Making
Planning supports decision making in its four main functional areas: production, harvest, storage, and distribution. Production activities include locating the land for a specific crop, timing sowing, and estimating the natural and industrial resources required for crop development. At harvest, the right reaping time, equipment, labor, and transportation activities are needed [22
]. In this work, we proposed a methodology that allows detecting commercialization opportunities in the future by employing the price analysis of the most important agricultural products that are commercialized in the Mexican wholesale food markets.
Regarding the span commercialization opportunities, Hass avocado, for example, has the highest prices between Week 26 and Week 34, which corresponds to the period between June and August. On the other hand, while the peak in its production, in Mexico, is in March, the nadir in its production is in August [23
]. This is consistent to the supply and demand dynamic. However, span commercialization opportunities are affected by other factors besides supply and demand. For example, the plant physiology (flowering and fruit development) plays an important role. While the blossoming period of “Normal” and “Marcena” cultivars’ flowering, which have a higher avocado production [24
], does not overlap with the span commercialization opportunity, a “local” flowering cultivar is productive during the span commercialization opportunity detected [24
]. This suggests that climate, latitude, soil, and agricultural varieties are factors that must be taken into account when trying to make the most of span commercialization opportunities.
Furthermore, low prices can also be detected (data not shown). This information may be critical for food processing industries, which may want to buy raw fruit and vegetables at their lowest prices.
Moreover, price forecasting for agricultural products has been performed before and data extraction depends heavily on the data availability from the country where it has been performed. For example, in Mexico, Marroquín Martínez and Chalita Tovar performed price forecasting for the price of tomato by extracting data from SNIIM and implementing Box–Jenkins methodology [6
]. This is the work with the most similar methodology to the one presented here. However, in the present work, the estimated models were less complex because MA order was at most 12 weeks while Marroquín Martínez and Chalita Tovar [6
] employed an AR order of 23 months.
In this work, future weekly prices for 18 different products were predicted. Short-term price forecast represents both risks and opportunities: they can result in harvesting delay when the trend suggests a decrease in prices, but, at the same time, it offers the opportunity to search different buyers for their products (e.g., process industry).
To validate the models, the first eight weeks of 2019 were employed (these weeks were excluded from the model estimation) in contrast with Li et al. where the train dataset was also the test dataset. Relative error ranged from 3.15% to 41.39% [5
]. It was considered that relative error smaller than 20% is useful for decision making, which includes the products: big cauliflower, big Valencia orange, epazote, Hass avocado, Italian zucchini, jicama, kiwi, lemon, lime, male banana, medium-sized pineapple, peanut, red grapefruit, and saladette tomato. The smallest prediction error was for red grapefruit with a 3.15% relative error.
The products with a prediction error greater than 20% are not priority for data analysis due their low economic impact [3
]. New methodologies and incorporation of additional datasets can contribute to the reduction of the relative error.
Span commercialization opportunities provide relevant information for production and harvesting planning. However future price forecasting represents an opportunity for the short-term agronomic management reaction.