Decision-making based on data analysis leads to knowing market trends and anticipating risks and opportunities. These allow farmers to improve their production plan as well as their chances to get an economic success. The aim of this work was to develop a methodology for price forecasting of fruits and vegetables using Queretaro state, MX as a case study. The daily prices of several fruits and vegetables were extracted, from January 2009 to February 2019, from the National System of Market Information. Then, these prices were used to compute the weekly average price of each product and their span commercialization in Q4 and over the median of historical data. Moreover, product characterization was performed to propose a methodology for future price forecasting of multiple agricultural products within the same mathematical model and it resulted in the identification of 18 products that fit the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model. Finally, future price estimation and validation was performed to explain the product price fluctuations between weeks and it was found that the relative error for most of products modeled was less than 10%, e.g., Hass avocado (7.01%) and Saladette tomato (8.09%). The results suggest the feasibility for the implementation of systems to provide information for better decisions by Mexican farmers.

MicroRNAs Sequencing for Understanding the Genetic Regulation of Plant Genomes | InTechOpen, Published on: 2016-07-14. Authors: Christopher A. Cedillo-Jiménez, Marcelo Hernández–Salazar, Tania Escobar-Feregrino, et

The large-scale conversion of forests to agriculture has caused biodiversity loss, climate change, and disrupted dietary fatty acid balances, with adverse public health effects. Wild edibles like pine nuts, especially Pinus cembroides, provide sustainable solutions by supporting ecosystems and offering economic value. However, variability in seed quality limits market potential, and lipidomic studies on P. cembroides remain sparse. This paper underscores the ecological, social, and nutritional value of P. cembroides while advocating for advanced research to enhance its use as a non-timber forest resource in Mexico’s communal areas. It explores various analytical techniques, such as nuclear magnetic resonances (NMR), chromatography coupled with mass spectrometry (HPLC-MS, GC-MS) and GC coupled with flame ionization detector (GC-FID), highlighting extraction methods like derivatization, purification, and thin-layer chromatography. Likewise, some considerations are addressed for the treatment of data obtained in the detection of fatty acids from bioformatics and the evaluation of the data through statistical methods and artificial intelligence and deep learning. These approaches aim to improve fatty acid profiling and seed quality assessments, fostering the species economic viability and supporting sustainable livelihoods in rural communities, encouraging researchers across the country to explore the fatty acid composition of different P. cembroides populations can drive valuable insights into its nutritional and ecological significance. Such efforts can enhance understanding of regional variations, promote sustainable use, and elevate the specie’s economic and scientific value.