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Texas A&M computer model: Melon Man helps predict harvest time

When melon producers judge harvest time for their fruit, they rely on experience and some general rules, but they admit it has been more of a guessing game than a scientific pursuit.

Yet buyers' contracts are signed months before harvest. Growers must schedule labor for harvest and arrange transportation for the produce to get to market. It is obvious that decisions must be made months before the fruit is ripe.

If there's an error made on the early side, the producer could have fewer, smaller and greener melons. If it's made on the late side, he could wind up with melons rotting in a field or selling them at half-price or less. Harvest timing plays a major role in determining produce prices in the marketplace.

Farmers would be more than grateful if there were only some way to scientifically predict the moment when a cantaloupe, honeydew or watermelon tastes the best and demands the highest price.

Enter Melon Man!

No, it's not someone who goes into the field and thumps and sniffs a cantaloupe, but a scientific computer model that can make the judgment for the farmer.

Jeff Baker, originally with Texas A&M University and now a plant physiologist with Agricultural Research Service in Big Spring, Texas, and with a team of researchers, came up with the model. He visited the Rio Grande Valley in 1998 and saw the problems facing producers, some of whom were growing up to a dozen different varieties of melons in one season.

Baker was able to develop a melon growth simulation computer model that removes a lot of the guesswork of time of harvest. Using information gathered in previous experiments, including those taking place in growth chamber and field, his team developed a simple temperature-driven crop model of melon to help commercial growers time crop events and predict harvest dates.

The model can simulate either a seeded or transplanted crop. Crop growth models simulate a different growing season scenario quickly, every second or faster.

By recording simple measurements, such as the rate at which the cantaloupe vine grows new leaf nodes in the soil, and by plugging in data from model-linked field weather stations, a producer can time the planting and harvesting of his crop.

Dave LaGrange, vice president and farm manager of Starr Produce Company in Rio Grande City was one of the first producers to take advantage of Melon Man.

To harvest the melons Starr grows, it is necessary to schedule 18 trucks along with 70 trailers that would be attached to them. Besides this, Starr must hire 350 pickers and 100 people to work in the packing plant. If the harvest is timed wrong, a lot of money can be lost.

At one time Starr Produce was growing a dozen different melons. The model helped the company decide on the three most predictable varieties of cantaloupe and one honeydew to grow.

LaGrange says, “There will always be a place for this cantaloupe model because new varieties come on the market all the time. I'll use the model whenever I try a new variety.”

Models for cotton, corn, soybeans, wheat and potatoes have also been developed. These are more complex models than those developed for melons, and predict timing of water, fertilizer and chemical applications.

Producers who are interested in using a model may contact the Alternate Crops and Systems Laboratory, 10300 Baltimore Ave., Bldg. 001, Room 342, BARC-WEST, Beltsville, MD 20705. There is no charge. For more information, call 301-504-5806.

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