Agri 2022

Crop monitoring and yield forecast models for maize production in South Africa and Poland under wet and dry seasons

Solomon W Newete

Solomon W Newete, Speaker at Agriculture Conferences
Agricultural Research Council, South Africa
Title : Crop monitoring and yield forecast models for maize production in South Africa and Poland under wet and dry seasons

Abstract:

Maize is one of the most prominent crops for human consumption in the world and a staple source of food supply that supports over 200 million people in developing countries. A third of the total areas under cereal cultivation in Africa is planted with maize. South Africa is the leading maize producer in the continent, and ranks one of the top ten in the world with a steady increase of production over the last century from a total of 328 000 tons in 1904 to a staggering record of 14.92 million tons in 2014 from 3 million ha of largely rain-fed farms. Irrigation only accounts for less than 1% of the total maize grown areas. The country is a net exporter of maize with about 25% of its production sold to countries in southern Africa. This study investigated crop growth monitoring and yield forecast in at the Agricultural Research Council-Natural Resources and Engineering (ARC-NRE) in South The unabated global warming is increasingly becoming worrisome for agriculture with substantial crop yield reduction already been reported globally. For instance, for every 1?C increase in global mean temperature, maize production decreases by 7.4%. Recurring drought incidences and climatic variabilities are often the main factors for crop yield reduction. The project investigated crop growth conditions and yield forecast using the Terra MODIS, Sentinel 2 and meteorological data. The MODIS database consisted of 100 crop maize fields with white and yellow maize crops cultivated in rain-fed and irrigated practices, in the Free State Province for a period of five years from 2015 – 2020. Crop yield, air temperature and rainfall data were acquired from the Agricultural Research Council to model the crop yield. The data were analyzed using the accumulated 8 days of NDVI (MOD09Q1) and accumulated 8 days’ differences between LST (MOD11A2) and air temperature (TA) from meteorological data. The preliminary results showed that the rapid increase in accumulated NDVI curve occurs at lower accumulated difference between LST and TA (∑LST-TA) and this resulted in high value of yield at the end of the season. During the dry season, however, the accumulated difference between LST and TA increased enormously resulting in lower rate of accumulated NDVI, thus this led to crop phenology occurring at different times. At good crop growing season, crop heading occurred earlier at lower accumulated difference in temperature (∑LST-TA) than in dry season and this has a direct response to crop yield.

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