Being able to accurately track crop development and then to describe and predict important stages of crop growth and development (crop phenology) and harvest dates is important for improving melon (Cucumis melo ‘reticulatus’ L.) crop management (e.g. fertilization, irrigation, harvest scheduling, pest management activities, labor, and machinery management, etc.). It is best to monitor and predict plant development based on the actual thermal conditions in the plant’s environment. Thermal conditions are a more reliable measure and predictive tool for plant development as opposed to a calendar, simply because plant growth is a direct response to temperature and environmental conditions.
Various forms of temperature measurements and units commonly referred to as heat units (HU), growing degree units (GDU), or growing degree days (GDD) have been utilized in numerous studies to predict phenological events for many crop plants (Baskerville and Emin, 1969; Brown, 1989; Baker and Reddy, 2001; and Soto, 2012). A graphical depiction of HU computation using the single sine curve procedure is presented in Figure 1 (Brown, 1989).
Twenty-five years ago we began working on the development and annual testing of a phenology model for desert cantaloupe production for Arizona conditions. The basic cantaloupe phenology model is shown in Figure 2 (Silvertooth, 2003; Soto et al., 2006; and Soto, 2012). Since cantaloupes are a warm season crop, we use the 86/55 ºF thresholds for phenological tracking.
This melon crop phenology model was developed under fully irrigated and well-managed conditions. That is important since non-irrigated fields are more likely to experience water stress, which significantly disrupts crop development patterns.
Key stages of growth or “guideposts” indicated in Figure 2 represent general average or “target” values that are subject to a slight degree of natural variation, which is normal.
Referencing the data from the Arizona Meteorological Network (AZMET) and several locations in the Yuma area, the HU accumulations (86/55 ºF thresholds) from 1 January 2025 to a set off our possible 2025 planting dates are listed in Table 1. The HU accumulations from 1 January 2025 to 30 March 2025 are listed in Table 2.
The HU accumulations after planting (HUAP) for these four possible planting dates for three Yuma area locations to 30 March 2025 are shown in Table 3. The HUAP values in Table 3 are simply the difference between the values in Tables 1 and 2. An example for the Yuma Valley, 15 January 2025 planting date is: 718.7 HU - 73.1 HU = 645.6 ~ 646 HUAP.
It is rather easy to test and evaluate this crop phenology model in the field under various planting dates, varieties, and conditions. The information in Table 3 can help serve as a reference to check for melon crop development in the field against this phenological model in Figure 2.
For melon crops in the lower Colorado River Valley, we would currently expect to find fields planted and watered up in mid-January to have small melons approaching golf-ball size and fields planted in early March should be starting to show fresh blooms soon.
Table 1. Heat unit accumulations (86/55 ºF thresholds) after 1 January 2025 on four possible 2025 planting dates utilizing Arizona Meteorological Network (AZMET) data for each representative site.
Yuma Valley: https://azmet.arizona.edu/application-areas/heat-units/station-level-summaries/az02
Yuma North Gila: https://azmet.arizona.edu/application-areas/heat-units/station-level-summaries/az14
Roll: https://azmet.arizona.edu/application-areas/heat-units/station-level-summaries/az24
Table 2. Heat unit accumulations (86/55 ºF thresholds) after 1 January 2025 to 30 March 2025 utilizing Arizona Meteorological Network (AZMET) data for each representative site.
Table 3. Heat unit accumulations (86/55 ºF thresholds) after planting (HUAP) from four possible 2025 planting dates and three sites in the Yuma area utilizing Arizona Meteorological Network (AZMET) data for each representative site. Each value is rounded to the next whole number. Note: the values in Table 3 are determined by taking the difference between the HUs for each representative site and four planting dates in Tables 1 and 2.
Figure 1. Graphical depiction of heat unit computation using the single sine curve procedure. A sine curve is fit through the daily maximum and minimum temperatures to recreate the daily temperature cycle. The upper and lower temperature thresholds for growth and development are then super imposed on the figure. Mathematical integration is then used to measure the area bounded by the sine cure and the two temperature thresholds (grey area). (Brown, 1989)
Figure 2. Heat Units Accumulated After Planting (HUAP, 86/55 °F)
First, I want to thank everyone who participated in last week's Vegetable Pest Losses Survey.
This year's survey included the return of the lettuce disease losses section. While several diseases were present and managed last season, downy mildew accounted for the majority of disease management costs by a wide margin. This really underscores the
impact that last spring's unusually rainy weather had on disease development across the Yuma lettuce production region.
No one can predict exactly what this upcoming spring will bring, but there has been discussion about the possibility of a strong El Niño leading to an extended monsoon season. If that proves true, the conditions would once again support spring
downy mildew development. The pathogen only needs about 3 to 4 hours of continuous leaf wetness to infect lettuce, so periods of overnight moisture, prolonged morning dew, or frequent rainfall when inoculum (spores) are present increase disease risk.
With that in mind, this seems like a good opportunity to review what is known about downy mildew and discuss strategies for its management.
Resistance in lettuce to Bremia lactucae, the causal oomycete pathogen behind downy mildew, is inherited in a gene-for-gene fashion, meaning one major gene product in the plant host interacts with one major gene product in the pathogen. When
resistance is present, this leads to an incompatible interaction between plant and pathogen and results in complete immunity to infection. Resistance genes in these types of interactions most often encode a protein molecule that acts like a burglar
alarm. These molecular sensors in the host bind to proteins secreted specifically by the pathogen, and when that happens a storm of defense responses is activated in the plant that excludes further infection. This is not the only mode of genetic resistance
found in plants, but it is often the most drastic and effective against obligate parasites like downy mildew.
But this simple gene-for-gene interaction often puts incredible selection pressure on the pathogen populations to change such that they can get around the resistance. By losing the molecule that the plant detects in order to initiate a defense response,
the pathogen becomes unrecognizable to the resistance genes a plant variety may have. Just like spraying the same mode of action over and over again leads to a pest population developing resistance to a pesticide, the same selection applies to genetic
resistance. The longer a resistance gene is deployed in a region, the more likely the pathogen population is to change in response until that resistance gene is no longer effective at managing the disease.
One of the biggest challenges with lettuce downy mildew is that B. lactucae is constantly changing over time. It exists as many different races, where each race has a different reaction to the resistance genes bred into lettuce varieties. You
can think of these races as different versions of the same pathogen. A lettuce variety that resists one race may still be susceptible to another.
These races are identified by testing them against a panel of lettuce varieties with known resistance genes. In the western United States, races are named by the International Bremia Evaluation Board-U.S. (IBEB-US) and are given names with a number followed by the country’s abbreviation, such as 8US, 9US, or 10US. The populations found in the western U.S. are different from those found in Europe, so each region uses its own independent naming system.
The downy mildew population has changed considerably over time. Earlier races (1US through 4US) are now rarely found in commercial lettuce production. During the 2000s and 2010s, races 5US through 8US became the most common. Race 9US was recognized after being detected repeatedly between 2015 and 2017, and the newest officially recognized race, 10US, was designated in 2025. Below is a pie chart showing the relative frequency of the races identified from 114 Yuma County downy mildew samples between 2023-2024:
Figure 1: Pathotyping results of 114 lettuce samples from Yuma County collected between 2023 and 2024. Data source: https://bremia.ucdavis.edu/bremia_database_main.php
The results show that much of the downy mildew population found in Yuma County is made up of novel strains of Bremia lactucae that have not yet been officially classified as a race. An official race is only recognized after it has been shown to be stable and widespread over multiple locations and growing seasons. These newer strains may disappear over time, or they may eventually become established and earn an official race designation. In the meantime, this means growers and lettuce breeders in Yuma County are often dealing with strains that can dodge the resistance in some lettuce varieties before those strains are common enough to be officially recognized. It also highlights why relying on resistance alone is not enough to manage the disease.
Table 1: Pathotyping and fungicide sensitivity results of samples from Yuma County collected in 2025.
This trend appears to be continuing. All of the downy mildew samples sent for race testing last season were identified as novel strains rather than known, officially designated races.
It's impossible to predict exactly how these new strains will respond to the resistance genes found in today's commercial lettuce varieties. However, because they have not been previously characterized, they are more likely to overcome existing genetic
resistance than the races we already know about.
New strains develop naturally over time. They can arise when different strains exchange genetics (i.e. intermate) or through random mutations. When growers plant varieties with similar resistance packages over large areas, the pathogen population
is placed under strong selection pressure. Any strain that happens to acquire the ability to infect those resistant varieties gains a major advantage and gets to reproduce without competition where other strains cannot. Over just a few disease
cycles, those successful strains can become much more common in the population until they are the predominant strain overall.
An important point to remember is that the resistance bred into commercial lettuce varieties is not wearing out or becoming weaker over time. The genetics in the lettuce remain just as effective as when the variety was released. What changes is the
pathogen. As the downy mildew population evolves new strains emerge that can bypass resistance genes that previously worked very well.
That means that varieties carrying resistance to races 5US through 10US are still doing exactly what they were designed to do. They continue to suppress those known races and help prevent them from becoming widespread in commercial fields. So, if
you experience significant downy mildew in a field planted with a variety that has a strong resistance package, the culprit is most likely one of these newer, uncharacterized strains rather than a failure of the variety itself.
Unfortunately, Bremia lactucae can evolve much faster than scientists can identify new races and breeders can develop and release resistant varieties. That's why no resistance package should be viewed as a stand-alone solution.
This is also why extension, researchers, and the seed and crop protection industries place so much emphasis on the integrated pest management (IPM) concept. Genetic resistance is an essential tool, but it works best and remains the most sustainable when combined with other management practices. For novel strains that can slip past host resistance, timely fungicide applications and other disease management strategies become especially important for maintaining control.
Figure 2: Mean disease severity by treatment. Disease severity was determined by rating 10 plants within each of the five replicate plots per treatment using the following rating system: 0 = no downy mildew present; 1 = downy mildew present on bottom leaves of plant; 2 = downy mildew present on bottom leaves and lower wrapper leaves; 3 = downy mildew present on bottom leaves and all wrapper leaves; 4 = downy mildew present on bottom leaves, wrapper leaves, and cap leaf; 5 = downy mildew present on entire plant. Disease severity is displayed as the mean of five replicates across all three lettuce varieties and bars show a 95% confidence interval around the mean calculated from individual treatment data. Compact letter display (CLD) above boxes show significantly different treatments (Kruskal-Wallis ANOVA and Dunn’s test). Boxes sharing the same letter(s) are not significantly different from one another. Fb = “followed by” in the rotation. Not all products are registered yet for use in lettuce. The inclusion of specific fungicide products or formulations in these trials does not constitute an endorsement or recommendation over other labeled products.
The most effective way to manage lettuce downy mildew is to use an integrated approach. Plant varieties with a strong resistance package against races 5US through 10US, and pair that resistance with timely, full-label-rate fungicide applications when environmental conditions favor disease. This combination provides the broadest and most reliable protection against both known races and the novel strains that continue to emerge in Yuma County.
If you have any concerns regarding the health of your plants/crops please consider submitting samples to the Yuma Plant Health Clinic for diagnostic service or booking a field visit with me:
Christopher Detranaltes, Ph.D.
Cooperative Extension – Yuma County
Email: cdetranaltes@arizona.edu
Cell: 602-689-7328
6425 W 8th St Yuma, Arizona 85364 – Room 109Interested in the latest automated weeding technologies? University of California Cooperative Extension will be hosting the 2024 Automated Technology Field Day where twelve of the newest commercial and academic thinning and weed control technologies will be demonstrated in the field. Featured technologies, some showcased for the first time to a general audience, include a high voltage electric weeder that kills weed seed pre-emergence, laser weeders (two types), “smart” precision spot sprayers (four types), “smart” in-row cultivators (four types), and the UA/UC Davis smart steam applicator for killing weed seed and soilborne pathogens pre-emergence. Company representatives and university personnel will be on hand to discuss their equipment. The event will be held from 9:00 am – 12:00 noon, Thursday, June 27th in Salinas, CA. For additional information, see the event flyer below.
This inquiry has been brought to us for different herbicides; How much time does this product require to safely plant lettuce?
Of course, this depends on many variables such as the management done in the crop before lettuce, texture, water applied, rate, climatic conditions presented, and many other factors.
The following table published in this Newsletter by Barry Tickes can serve as a general guideline to base Integrated Pest Management decisions and program our strategies. It’s always recommended to check the label for the product used in previous crops.
Preliminary results from a field trial that is being conducted at the Yuma Agricultural Center experimental farm to evaluate seven bioinsecticides against whiteflies in broccoli demonstrate that M-Pede (Potassium salts of fatty acids aka insecticidal soap), BotaniGard 22WP (Beauveria bassiana Strain GHA), Pyganic (Pyrethrins), Surround (Kaolin clay), and Venerate (Burkholderiaspp. Strain A396) may favor measurable control of the pest. These insecticides exhibited a reduction in whitefly adults and nymphs density relative to the nontreated control (Figure 1). The trial is ongoing, and more data will be collected to assess the efficacy of these insecticides further. Potassium salts of fatty acids are produced by adding potassium hydroxide to fatty acids found in animal fats and in plant oils. Pyrethrins is a mixture of six active compounds extracted from Chrysanthemum cinerariifolium plants. Beauveria bassiana is a naturally occurring entomopathogenic fungus. Burkholderia spp. strain A396 is a naturally occurring entomopathogenic bacteria. Kaolin clay is a soft, white, naturally occurring clay mineral.
Figure 1. Means whitefly adults (A), small whitefly nymphs (B), large whitefly nymphs (C),
and total whitefly nymphs (D) as affected by bioinsecticide applications. Bars with the
same letters are not statistically significant.
The combination of two separate processes whereby water is lost on the one hand from the soil surface by evaporation and on the other hand from the crop by transpiration, is referred to as evapotranspiration (ET). Evaporation is the process whereby liquid water is converted to water vapor (vaporization) and removed from the evaporating surface (vapor removal). Water evaporates from a variety of surfaces, such as lakes, rivers, pavements, soils, and wet vegetation. Energy is required to change the state of the molecules of water from liquid to vapor. Direct solar radiation and, to a lesser extent, the ambient temperature of the air provide this energy (Fig:1).
Figure 1: Key factors driving ET in agriculture include solar radiation, temperature, wind,
humidity, and plant and soil water loss.
Transpiration consists of the vaporization of liquid water contained in plant tissues and the vapor removal to the atmosphere. Crops predominantly lose their water through stomata. These are small openings on the plant leaf through which gases and water vapor pass. The water, together with some nutrients, is taken up by the roots and transported through the plant (Fig: 2).
Figure 2: A diagram illustrates the process of transpiration in plants, where water is
absorbed by roots, moves through the stem, and evaporates from the leaf surfaces.
Why evapotranspiration (ET)?
ET is the largest component of the hydrological cycle and is one of the most critical variables in irrigation management, crop production, and the sustainability of agriculture. It has a direct impact on water resources availability and use, water quality, and the earth’s energy balance. Daily evapotranspiration (ET) rates are needed for irrigation scheduling. The evapotranspiration rate is normally expressed in millimeters (mm) or inches (in) per unit of time (e.g., an hour, day, week, month, or even an entire growing period or year).
Reducing evapotranspiration (ET) can lead to significant water savings by minimizing unnecessary water loss through soil evaporation and plant transpiration. By applying irrigation more efficiently only when and where it's needed, plants can maintain optimal moisture levels, reducing stress and promoting better growth. This can enhance nutrient uptake, improve plant health, and ultimately lead to higher yields. Moreover, conserving water through reduced ET is especially critical in arid regions, where freshwater resources are limited, and efficient water use is essential for sustainable agricultural production.
Below are strategies to reduce ET
Figure 3: Figure 3. Soil moisture sensor installation at the Valley Research Center,
Yuma, AZ.
VegIPM Update Vol. 17, Num. 15
July 22, 2026
Results of trap catches below!!
Whitefly: Adult activity remains steady across locations; above average for this time of the year, especially high numbers seen in North Gila Valley. Historically, whitefly numbers peak in July.
Thrips: Adult thrips activity remained low over the last two weeks. About average for this time of the year. Historically, thrips numbers remain low until Sept-Oct.
