The Weather as an Indicator for Decision-Making Support Systems Regarding the Control of Cutworms in Beets and Cereal Leaf Beetles in Cereals and Their Adoption in Farming Practice

Abstract

The requirements of integrated control clearly indicate the need to use all available non-chemical methods to protect crop plantations, before deciding on a chemical method. In addition, there is also a provision in the integrated protection guidelines about the need for monitoring and signalling of pests and the recommendation to use available decision support systems (DSS) in chemical protection. The decision-support system facilitates and assists the agricultural producer in fulfilling the above principles and guidelines. The Institute of Plant Protection-National Research Institute (IPP–NRI) has conducted a six-year research program to develop a scientific basis for supporting decisions to control cutworms (Agrotis segetum Schiff et. Den., and Agrotis exclamationis L.) in sugar beets and cereal leaf beetles (Oulema spp.) in cereals. Validation tests in field experiments have demonstrated their effectiveness and their suitability for agricultural practice. Thanks to the obtained results, farmers have access to knowledge supported by solid research and field tests. Based on the obtained results, applications have been developed that will be usable by producers. The applications will assist in deciding the need for chemical protection of sugar beet against cutworms and cereals against cereal leaf beetles, thus recommending treatment only when necessary.

1. Introduction

1.1. Strategic Crops for Agriculture in Poland

Cereal production is of strategic importance to the economy both in Poland and worldwide. Globally, cereals account for about 50% of crop production [1]. They are a staple food and increasingly a renewable raw material for industrial, energy or pharmaceutical purposes. In Poland, the area sown to cereals in recent years has averaged 7.5 million hectares, while their share in the sowing structure is just over 70% [2]. The largest share of wheat (Triticum aestivum L.) is cultivated (approximately 32%).

The area of sugar beet (Beta vulgaris L.) cultivation in Poland is approximately 0.25–0.3 million ha [2]. Sugar beet is the second economically important plant grown for obtaining sugar. In addition to the production of sugar, both in Poland and in Europe, sugar beet is used for industrial purposes to produce e.g., biomethane and biogas, food syrup and for the production of ethyl alcohol.

1.2. Decision Support System

A vital part of integrated plant protection is to regularly monitor and report the occurrence of plant diseases and pests. This is done to assess current pest threat levels. In addition, integrated plant protection principles require field observations to detect increases in disease severity and pest populations before chemical control treatments are launched. Such monitoring allows pest detection at early development stages when pest populations are still small. At such times, it is easier to minimize crop damage and the best time for chemical treatments relative to economic harm thresholds.

Decision support systems (DSS) are sets of instructions aimed at aiding farmers and consultants to reach decisions to apply chemical treatments based on information about pests (pest biology, life cycle, harmfulness), weather conditions and economic factors. The system proves to be superior in ensuring the best timing of measures and best pest control effectiveness as compared to simply increasing doses of active substances used as control agents. DSS supports decision-making and encourages the use of integrated plant protection against pests, diseases, and weeds. However, the final decision always rests with the farmer. There are many definitions of decision-making systems in the literature, Refs. [3,4] DSS is described as “a computer-based support system for decision makers who deal with semistructured problems to improve the quality of decisions”. Other authors [5] specified DSS as “a human-computer system which is able to collect, process, and provide information based on computers”. Many researchers [6,7] described decision systems in agriculture as “a specific class of computerized information system, enabling to manage decision-making activities”. In conclusion, it can be said that decision-making support systems are environmentally friendly as they reduce the volume of plant protection products in use, machine wear and tear, and labor while boosting farm efficiency and competitiveness [8,9].

In recent years, some of the systems used in potato cultivation in Poland and Germany are, for example, systems for controlling the potato beetle [10] or the NegFry system - a decision support system for protecting potatoes against potato blight [11].

1.3. Life Cycles, Harmfulness

1.3.1. Cutworms

The two most numerous cutworm species in sugar beet holdings are Agrotis segetum Schiff et. Den. (turnip moth in its adult form) and Agrotis exclamationis L., (heart and dart moth) of the owlet moth (Noctuidae) family. Adult moths have a wingspan of 25 to 55 mm with gray-brown forewings with stigmata that, depending on the species, may be clear or blurred-bordered and round, kidney, tenon or wedge shaped. Their hind wings are brighter, almost snow-white with a subtle sheen. The eggs are initially whitish or slightly cream-colored, then red and, just before hatching, dark red with purple or brown tinting. The eggs are 0.5 to 0.9 mm in diameter and richly and distinctively carved with multiple radial ribs. The caterpillars go through six larval stages. Older caterpillars may reach the size of 30 and 65 mm. The family’s distinctive feature is that the caterpillars curl up to rest or when disturbed. The pupa, which measures 16 to 20 mm in length, is usually rusty-red to brown in color. It has two sharp appendages and depending on the species, one or two papillae on either side. The pupae may also come with dorsal spines. Fully-grown caterpillars overwinter in the soil, typically in their L₅ and L₆ stages, at the depth of 25–30 cm, or as pupae. In spring, once temperatures exceed 10 °C, pupation takes place in the soil at the depth of 5–10 cm, after which butterflies emerge [12,13,14].

The harmful form are voracious caterpillars, which go through six larval stages. Emerging, young beet plants are the most vulnerable to harm from such pests. The caterpillars nibble on beet roots near the root collar effectively cutting the plant off from its roots. Once damaged, the plant falls over and either dies or is dragged into the ground by caterpillars to be devoured at night. The initial damage is seen on leaves in the form of small, regularly-shaped openings. As caterpillars grow, the harm they inflict extends to underground plant parts. Cutworm infestation damage begins with plants growing along the field perimeter. Harmfulness of the turnip moth and the heart and dart moth is determined first of all by the population size of the cutworm, which is affected largely by the weather conditions. The reduction of sugar in the beet root as a result of feeding by cutworms can be about 2–3% [15,16].

1.3.2. Cereal Leaf Beetle

The cereal leaf beetle species that occur in Poland are Oulema melanopus (L.), O. duftschmidi (L. Redt.) and Oulema gallaeciana Heyden, all belonging to the Chrysomelidae family. The species Oulema melanopus or O. duftschmidi are practically indistinguishable by their external features and highly similar in their biology and harmfulness. Oulema melanopus and O. duftschmidi beetles grow to 6 mm in length. Their wing elytra are oblong, blue-green in color with a metallic sheen and slightly indented depressions. Their pronotum and legs are reddish brown, and their feet are black. Oulema gallaeciana beetles are slightly smaller, reaching up to 5 mm in length. Their wing elytra are dark blue. Their pronotum and legs are black. Their eggs are honey-yellow colored, about 1 mm in length, cylindrically shaped and bluntly rounded at both ends. The females typically lay each egg separately or two or three at a time, either along the leaf veins near the leaf base or in the leaf blade middle, usually on outer side. Cereal leaf beetle larvae are brown-yellow. They are soft and spindle-shaped. Their bodies are covered with mucus and feces, which makes them resemble small snails. They pupate in soil in cocoons at the depth of up to 5 cm below ground level. The Oulema melanopus and O. duftschmidi larvae pupate on plants in cocoons made of foamy white secretions. Their beetles overwinter in leaf litter, turf or between roots. In spring, once air temperature exceeds 10 °C for 2 to 3 days, they fly off to seek their host plants [12,13,14].

The damaging stage in their development is that of voracious beetles and larvae. In the spring, damage occurs on cereal leaves that end up having distinctive narrow, oblong openings along leaf veins bitten through them. Such damage is caused by foraging beetles. Where their numbers grow, the damage may disrupt proper plant growth. Later (in late May and early June), cereal leaves are damaged by the larvae which scrape off the outer leaf epidermis and eat the parenchyma along the veins without damaging underlying leaf epidermis. After some time, deeper leaf blade skin dries up and turns white as a result of leaf larvae feeding. Where larvae numbers are substantial, the assimilation area of flag and sub-flag leaves can be reduced by 20–50%. The decrease in the assimilation surface weakens the plant and the nutritional state of the caryopsis. In addition to direct losses in the form of reduced grain yields, cereal leaf beetles undermine field health. This is because viruses, bacteria and fungi penetrate damaged plant tissues causing many dangerous cereal diseases, which reduce not only yields but also crop quality.

The following overview outlines the scientific basis of decision-making systems for controlling such pests. The conclusions rely on many years of research on the development of such pests in controlled and field weather conditions based on total effective temperatures.

1.3.3. Available Pest Monitoring and Reporting Methods (Cutworm, Cereal Leaf Beetle)

Soil pests are becoming increasingly widespread causing substantial harm to sugar beet roots. Cutworms form a unique group of phytophages with such bioecological attributes as highly dynamic populations and are hidden in the soil in which the harmful larva develops either completely or partially while the soil serves as a natural protective barrier that prevents their control. For this reason, it is difficult to determine not only the current stage of soil pest development during the growing season, but also the optimal timing for eradication measures [17]. Therefore, comprehensive methods and recommendations for the protection of agricultural crops against cutworm damage are urgently needed. Due to their protracted development, cutworm larva numbers are known to fluctuate widely. During their rapid development stage, the caterpillars become so numerous that the host plants are completely overwhelmed and destroyed.

Timing chemical Oulema spp. control measures is not an easy task due to the extended time of oviposition and larvae hatching. This often prevents cereal farmers from timing chemical treatment properly. They often choose to control Oulema spp. after the feeding larvae have already destroyed a significant part of leaf assimilation surfaces. Delayed measures are far from cost effective. Farmers incur the cost of protecting their cereal plants without the full benefit in the form of salvaged crops.

To precisely time chemical Oulema spp. control measures, research has been launched on improved short-term forecasting with the use of multiple regression.

Timing plant protection product application is a daunting task. To do it successfully, extensive knowledge of the harmful organisms found on plants, their morphology, biology, plant damage symptoms and development in specific climatic conditions and habitats is required. Pest type and pest population sizes can be assessed with basic pest monitoring tools. Some such tools are as simple as a magnifying glass, a stereo microscope, an insect scoop, a yellow vessel, a colored adhesive board and a pheromone trap. More sophisticated tools include computer applications for timing treatments and automatic weather stations. The tools that help monitor and report the presence of cutworms and cereal leaf beetles include:

-yellow vessels, are commonly used to monitor pests in cereals (such as cereal leaf beetle) and other crops (rapeseed, corn). A yellow vessel is filled with water with the addition of a surface tension-reducing liquid to prevent insects from escaping. Such vessels are placed approx. 20 m into the field, always at plant top height. As plants grow, vessel placement height needs to be adjusted. The vessels need to be checked regularly at least twice a week, and at best daily, always at the same time. Small holes should be drilled near the vessel edge to prevent water from overflowing with the trapped insects,

-colored sticky boards–the color, usually yellow, is used to attract insects, while the adhesive acts as a trapping agent. The adhesive should be colorless and odorless and remain sticking regardless of the weather. The sticky boards should be inspected regularly, at least twice a week. The boards should be placed just above plant tops and elevated as the plants grow. Yellow sticky boards are used to control flight patterns and populations of cereal leaf beetles as well as other cereal, rapeseed, and corn pests,

-pheromone traps relying on synthetic compounds mimicking the scents of pheromones, which are hormonal substances secreted by female insects to which same-species males respond. Pheromone traps are used to control the European corn borer, the western corn rootworm and cutworms. The traps are placed in fields on stands at randomly selected locations at plant top height. They are inspected at least twice a week, always at the same time of day,

-light traps are used to catch moths of e.g., cutworms and European corn borers. The insects are attracted to light from a fluorescent lamp powered by alternating currents. Traps are suspended at 1.4 m above ground. The moths are caught from spring to autumn, dusk to dawn. The traps are checked for insects at least twice a week [18].

Pest occurrence observation findings need to be used adequately to help mitigate harm risks and prevent needless and excessive use of chemicals. They also help time treatments optimally in view of the financial cost of potential damage and make informed decisions as to whether to apply treatments.

2. Material and Methods

For short-term forecasting of cutworm occurrence, cutworm moth arrival at holdings should be monitored from early May. Moths’ flight times depend largely on the weather in a given year. In the spring, treatments are timed on the basis of the number of butterflies caught with light and/or pheromone traps. The date of treatment is set at 30–35 days (depending on weather conditions) after more than one butterfly is first caught within 2–3 days. Insecticide application timing may also be based on air temperature readings, i.e., total temperatures (from the date on which the butterflies first appearance masse until the total temperature reaches 501.1 °C) and total effective temperature (i.e., the sum of the physiological threshold air temperatures, i.e., temperatures exceeding 10.9 °C, from the day of first mass moths appearance until the total temperature reaches 230.0 °C). A slight delay of a few days in the application of chemicals will not compromise control effectiveness. The best chemical treatment results are obtained when cutworms reach the L₂ stage, and the plants reach the inter-row covering (rosette development) stage.

The identification of the best cutworm control measure timing has been based on several years of research in a controlled environment (a growth chamber at the IPP-NRI) and in the fields of the Field Experimental Station of the IPP-NRI in Winna Góra). Once calculations from the entire experimental material became available upon study completion, they were summarized in

Table 1. Winna Góra research findings accounted for in the cutworm (by trap baits) control study for 2003–2008.

Year	Critical Date	Total Temperature (TT) over 30 Days	Average Temperature (AT) over 30 Days	Total Temperature (TT) over 35 Days	Average Temperature (AT) over 35 Days
Field conditions–trap catches
2003	19.5	584.6	19.5	668.2	19.1
2004	26.5	469.6	15.6	538	15.4
2005	8.6	573.1	19.1	689.9	19.7
2006	25.5	485.5	16.2	598.4	17.1
2007	20.5	602.1	20.4	694.9	20.2
2008	22.5	511.8	17.1	608.8	17.4

and

Table 2. Winna Góra research findings accounted for in the Agrotis spp. control study for 2005–2008.

Year	Number of Days	Total Temperature (TT) [°C]	Average Temperature (AT) [°C]	TET = TT-10.5 + AT–11.1 * [°C]	TET = TT-10.9 ** [°C]	Average Moisture [%]
Field breeding
Agrotis sp.
2006	22.4	490.5	21.8	246.8	246.5	65.3
2007	29.1	541.1	18.5	222.7	223.5	77.9
2008	25.5	456.8	17.9	176.3	178.5	62.9
Phytotron breeding (temp. 17° + 20° + 24°C)
Agrotis exclamationis (heart and dart)
2005	21.3	461.1	21.7	228.4	228.7	55.8
2006	28.1	565.2	20.1	258.4	259.3	59.2
2007	26.5	519.2	19.9	230.1	230.5	59.3
2008	27.6	543.8	20.1	242.0	242.7	57.6
Agrotis segetum–turnip moth
2005	20.2	449.5	23.3	-	229.2	63
2006	28.8	554.1	19.3	-	237.3	65.1
2007	22.0	430.1	20.1	-	244.2	60.4
Mean total effective temperature in field and controlled environments (2009) 230.0 °C
Average days in field and controlled environment (2009) 25.1
Average total temperature in field and controlled environments (2009) 501.1 °C

* TET–calculated for egg and larvae physiological zero A. exclamationis. ** TET-calculated for the mean physiological zero Agrotis spp.

, which includes growth chamber and field results for two cutworm species (Agrotis exclamationis and Agrotis segetum). The results include critical butterfly flight dates for 2003–2008. Temperature totals were provided for 30 and 35 days after butterfly flight dates with temperature averages for such periods added for comparison. The timing of A. exclamationis development in the field environment was determined for the years 2003–2008. Averages were also provided for that period for total temperatures, mean temperature and mean humidity and total effective temperature. These were calculated separately for egg incubation and caterpillar development to stage L₂. Once the two stages of development were summed up, values of the above-mentioned weather features were determined for the examined cutworm development periods.

The total effective temperature was based on the previously established threshold temperature for heart and dart eggs and caterpillars. As for the use of total effective temperature to control cutworms, the average physiological zero was determined for both the eggs and the caterpillars, calculated as follows:

• In the years of field research, heart and dart egg incubation lasted an average of 7.0 days. This number was multiplied by the physiological zero for eggs, i.e., by 10.5 °C (7.0 × 10.5 = 73.50).
• Caterpillar development lasted 19.7 days. This number was multiplied by the physiological zero for caterpillars, i.e., by 11.1 °C (19.7 × 11.1 = 218.67).
• The two values were then summed up and divided by the number of days in the relevant development cycle, which averaged 26.7 days (73.50 + 218.67 = 292.17:26.7 = 10.94).

Table 1 and Table 2 also shows the results of the controlled environment tests carried out in phytotron chambers. The average development timing tests in the phytotron took place between 2005 and 2008 for A. exclamationis and between 2005 and 2007 for A. segetum. Due to unfavorable weather during the turnip moth departure in 2008, this species is not included in the table. The averages for this period, i.e., total temperature, mean temperature, mean humidity, and total effective temperature were calculated for the natural conditions (total effective temperature in the phytotron for A. segetum are not provided due to the lower physiological zeros for the eggs and caterpillars of this species):

• The critical moth flight date was determined to be May 22.
• The shortest development period in cutworm breeding stations was determined to be 25 days.
• Minimum total temperature: 501.4 °C.
• Minimum total effective temperature: 229.2 °C, with due account taken of the physiological zeros for eggs and caterpillars for each tested species.
• Minimum total effective temperature of 230.0 °C, with due account taken of the mean physiological zero of 10.9 °C.

The decision support system is used to identify the best timing of the chemical control of cereal leaf beetles. In a given growing season, it is critical to observe and record the date of mass oviposition by cereal leaf beetles (in practice, this is also the time of the first hatchings of individual larvae from previously laid eggs, which are about 1 mm in size) and, starting on that date, to start recording the average daily air temperature and humidity.

Therefore: (1) for each farm, the mass oviposition stage (or the time of hatching of the first cereal leaf beetle larvae measuring ca. 1 mm from the earliest laid eggs) should be entered into the application.

(2) From this date onward, the average daily air temperature and the average daily air humidity should be entered into the application.

However, if, following that date, temperatures fall below the threshold temperature (10.6 °C), the effective temperature for such a day will be 0.0 °C (pest development will be inhibited but not regressed). Moreover, x1, i.e., the total of effective temperature + 5 days will not increase because the average from the previous days will then be lower than that from the preceding day as one of the values in the total will be 0.0. It is possible to assume that from the date on which mass egg laying is observed and on which this date is entered into the application, the said weather data will be entered automatically, provided that the application corrects negative values of effective temperatures (ET) by replacing them with 0.0, and that starting on this date, regardless of the average daily air temperature, ε is calculated continuously until the day on which the value e is the lowest in view of the absolute value.

The mathematical model in the form of a multiple curvilinear regression equation takes the following form:

$$y\left(j\right)=87.4+0.0984x1-0.0049x2-2.39x3+0.0185x\frac{2}{3}$$

where:

y(j): anticipated number of egg incubation days,

x1: total effective temperature + 5 anticipated days. The reason the treatment date was shifted by five days was to allow for a check of circumstances in a specific field and prepare for the treatment. To that end, on a given day, five times the average effective temperature was added to the calculated total effective temperature, such average effective temperature having been calculated by dividing the total effective temperature by the number of days elapsed since the mass oviposition was observed,

x2: average effective temperature: the total effective temperature for a given day divided by the number of days elapsed since the mass oviposition to arrive at the average effective temperature,

Total effective temperatures were calculated with account taken of the physiological zeros for the egg stage in the development of the cereal leaf beetle (10.6 °C) and Lema cyanella (10.2 °C) (when, in earlier studies, both of these cereal leaf beetle species were grown in insulators located in wheat fields, egg laying was found to occur at the same time, whereas the development period of Lema cyanella was shorter than that of the cereal leaf beetle). The physiological zero was adopted and used in studies based on literature [19].

x3: average air humidity: total average daily air humidity in the time from mass oviposition to a given day divided by days elapsed.

x$\frac{2}{3}$: mean air humidity square.

3. Results

All studies carried out for the purposes of short-term forecasting of cutworm occurrence focused on determining total temperature and total effective temperature to support choices of best treatment timing. In both phytotron and field environments, both of these totals significantly affected the duration of cutworm development from freshly laid eggs to the L₂ stage as caterpillars.

In 2007, using the reporting method, the time of treatment was set to be the 30th day after the critical date determined on the basis of moth flight timing. No further days were added as the average temperature of 20.1 °C for this period compared to previous years was found to be optimal for caterpillar development. The treatment was scheduled for June 19 (

Table 3. Weather data used to arrive at treatment date in Winna Góra in 2007.

Day	Average Daily Air Temperature	Total Temperature	Effective Temperature	Total Effective Temperature	Average Temperature from Critical Day to Treatment	Average Daily Air Moisture	Average Moisture from Critical Date to Treatment
21.05	22.3	22.3	11.4	11.4	22.3	69.1	69.1
22.05	23.1	45.4	12.2	23.1	22.7	69.4	69.3
23.05	18.6	64	7.7	31.3	21.3	77.7	72.1
24.05	18.8	82.8	7.9	39.2	20.7	67.3	70.9
25.05	23.4	106.2	12.5	51.7	21.2	60.5	68.8
26.05	21.7	127.9	10.8	62.5	21.3	72	69.3
27.05	22.3	150.2	11.4	73.9	21.5	70.3	69.5
28.05	22.4	172.6	11.5	85.4	21.6	63.8	68.8
29.05	20.6	193.2	9.7	95.1	21.5	62.2	68.0
30.05	12.8	206	1.9	97.0	20.6	85.7	69.8
31.05	15	221	4.1	101.1	20.1	77.8	70.5
1.06	17.3	238.3	6.4	107.5	19.9	75.7	71.0
2.06	16.3	254.6	5.4	112.9	19.6	94.5	72.8
3.06	14.5	269.1	3.6	116.5	19.2	95.7	74.4
4.06	15.9	285	5.0	121.5	19.0	96.2	75.9
5.06	18.2	303.2	7.3	128.8	19.0	85.7	76.5
6.06	20.5	323.7	9.6	138.4	19.0	78.6	76.6
7.06	22.6	346.3	11.7	150.1	19.2	61.1	75.7
8.06	23.6	369.9	12.7	162.8	19.5	54.1	74.6
9.06	21.4	391.3	10.5	173.3	19.6	64.4	74.1
10.06	20.7	412	9.8	183.1	19.6	65.2	73.7
11.06	21.5	433.5	10.6	193.7	19.7	57.7	72.9
12.06	23.3	456.8	12.4	206.1	19.9	50.7	72.0
13.06	21.2	478	10.3	216.4	19.9	70.4	71.9
14.06	22.5	500.5	11.6	228	20.0	68.3	71.8
15.06 (day 26)	24.8	525.3	13.9	241.9	20.2	62.4	71.4
16.06 (day 27)	19.4	544.7	8.5	250.4	20.2	77.6	71.6
17.06	20.4	565.1	9.5	259.9	20.2	60.4	71.2
18.06	18.5	583.6	7.6	267.5	20.1	78.8	71.5
19.06 (day 30)	18.5	602.1	7.6	275.1	20.1	76	71.6

). The treatment date for the other (phenological) method was arrived at based on the total average daily air temperatures for at least 24 days starting on the critical date set for May 20. The resulting total temperature was 478.0 °C. As this temperature was short of the total temperature threshold of 504.1 °C, temperatures from the following days continued to be added until the 504.1 °C threshold was reached. Day 26, i.e., June 15, was the first day on which the total temperature exceeded that threshold reaching 525.3 °C. The total effective temperature was then checked and found to be 241.9 °C on that day, i.e., below the prescribed value. On June 16, the total effective temperature reached 250.4 °C. As the totals exceeded the set threshold, the treatment was scheduled for that day by the phenological method. This was the 27th successive day after the critical date.

In 2008, the reporting method was used to schedule the treatment on the 35th day following the next day after the critical date. This was because, given that the average 30-day temperature was 17.1 °C, and in view of additional methodological assumptions, five additional days needed to be added to compensate for the cooling. Hence, the treatment was scheduled for June 26 (

Table 4. Weather data used to arrive at treatment date in Winna Góra in 2008.

Day	Average Daily Air Temperature	Total Temperature	Effective Temperature	Total Effective Temperature	Average Temperature from Critical Day to Treatment	Average Daily Air Moisture	Average Moisture from Critical Date to Treatment
23.05	13.6	13.6	2.7	2.7	13.6	79.9	79.9
24.05	13.9	27.5	3	5.7	13.8	78.6	79.3
25.05	12.6	40.1	1.7	7.4	13.4	79.8	79.4
26.05	14.6	54.7	3.7	11.1	13.7	69.6	77.0
27.05	16.8	71.5	5.9	17	14.3	77.8	77.1
28.05	14.8	86.3	3.9	20.9	14.4	54.3	73.3
29.05	15.8	102.1	4.9	25.8	14.6	44.7	69.2
30.05	18.6	120.7	7.7	33.5	15.1	49.5	66.8
31.05	19.9	140.6	9	42.5	15.6	50.7	65.0
01.06	20.2	160.8	9.3	51.8	16.1	53	63.8
02.06	21.2	182	10.3	62.1	16.5	45.5	62.1
03.06	21.2	203.2	10.3	72.4	16.9	44.9	60.7
04.06	18.5	221.7	7.6	80	17.1	57	60.4
05.06	16.7	238.4	5.8	85.8	17.0	61.2	60.5
06.06	18	256.4	7.1	92.9	17.1	58.9	60.4
07.06	20.4	276.8	9.5	102.4	17.3	46.7	59.5
08.06	20.8	297.6	9.9	112.3	17.5	54.8	59.2
09.06	21.3	318.9	10.4	122.7	17.7	57.1	59.1
10.06	20.8	339.7	9.9	132.6	17.9	55.6	58.9
11.06	17.1	356.8	6.2	138.8	17.8	58.6	58.9
12.06	15.1	371.9	4.2	143	17.7	54.2	58.7
13.06	13.3	385.2	2.4	145.4	17.5	75.7	59.5
14.06	12.8	398	1.9	147.3	17.3	70.8	60.0
15.06	13.3	411.3	2.4	149.7	17.1	66.7	60.2
16.06 (day 25)	13.2	424.5	2.3	152	17.0	86.6	61.3
17.06	16	440.5	5.1	157.1	16.9	65.1	61.4
18.06	17.5	458	6.6	163.7	17.0	55.5	61.2
19.06	19.6	477.6	8.7	172.4	17.1	59.7	61.2
20.06	18.1	495.7	7.2	179.6	17.1	68.5	61.4
21.06 (day 30)	16.1	511.8	5.2	184.8	17.1	69.5	61.7
22.06	20.8	532.6	9.9	194.7	17.2	60.9	61.7
23.06	21.6	554.2	10.7	205.4	17.3	65.9	61.8
24.06	16.4	570.6	5.5	210.9	17.3	55.5	61.6
25.06	17.7	588.3	6.8	217.7	17.3	61.2	61.6
26.06 (day 35)	20.5	608.8	9.6	227.3	17.4	67	61.7
27.06	18.9	627.7	8	235.3	17.4	59.3	61.7
28.06 (day 37)	16.6	644.3	5.7	241	17.4	73	62.0

). The phenological treatment was timed based on the total average daily air temperature over a minimum of 25 days starting on the day immediately following the critical date. The total temperature arrived at in this manner was 424.5 °C. However, given that this value was short of the temperature total of 501.4 °C, temperatures continued to be added on the following days until the 504.1 °C threshold was reached. On the 30th day, i.e., on June 21, the total temperature reached 511.8 °C thus exceeding the threshold for the first time. However, the total effective temperature on that day did not reach the threshold and amounted to 184.8 °C. On June 28, the total effective temperature reached 241.0 °C and thus for the first time passed the threshold with the mean physiological zero taken into account. The treatment was scheduled for this day, which was the 37th day after the critical date.

The experimental work led to the formulation of a regression equation for use in determining the timing of cereal leaf beetle control treatments. The practical application of the algorithm for combating cereal leaf beetles helped avoid mistakes commonly made by farmers (who tended to apply their measures too late). The fact that the treatment date calculated with the use of the equation was only two days late in one of the six years of the study suggests its usefulness in verifying treatment time directly in the field by forecasting treatment date five days in advance.

On the 12th day of development of mass-laid cereal leaf beetle eggs, i.e., on May 25, ε equaled 0.16. After the predicted five days were added to May 25, treatment date was pushed forward to May 30, which should coincide with the mass hatching of larvae from the mass-laid eggs. The actual mass larvae hatching was observed on May 28 (

Table 5. Identifying best timing of cereal leaf beetle control measures in 2007 in Winna Góra with the use of the regression equation based on the duration of egg incubation periods for both species of Oulema spp.

Date	Average Daily Air Temperature	Average Daily Moisture	Total Effective Temperature	x1 Total Effective Temperature + 5 Days	x2 Average Effective Temperature	x3 Average Air Moisture	y	y(d)	y(p)	e	Date	Time of Treatment Based on:
14.05	21.2	75.2	10.6	63.6	10.6	75.2	8.49	1	5	2.49	14.05
15.05	15.5	86.7	15.5	54.3	7.8	81.0	13.14	2	5	6.15	15.05
16.05	12.9	71.9	17.8	47.5	5.9	77.9	12.54	3	5	4.54	16.05
17.05	9.4	89.9	17.8	40.1	4.5	80.9	14.86	4	5	5.86	17.05
18.05	10.5	83.5	17.8	35.6	3.6	81.4	15.58	5	5	5.59	18.05
19.05	16.4	59.6	23.6	43.3	3.9	77.8	13.96	6	5	2.96	19.05
20.05	20.6	68.3	33.6	57.6	4.8	76.4	13.92	7	5	1.92	20.05
21.05	23.6	70.9	46.6	75.7	5.8	75.8	14.44	8	5	1.44	21.05
22.05	25.3	66.8	61.3	95.4	6.8	74.8	15.04	9	5	1.05	22.05
23.05	18.8	76.8	69.5	104.3	7.0	75.0	15.86	10	5	0.87	23.05
24.05	19.7	64.2	78.6	114.3	7.1	74.0	16.31	11	5	0.32	24.05
25.05	24.7	63.7	92.7	131.3	7.7	73.1	17.15	12	5	0.16	25.05	+ 5 days
26.05	23.4	75.8	105.5	146.1	8.1	73.3	18.29	13	5	0.31	26.05
27.05	22.5	75.9	117.4	159.3	8.4	73.5					27.05
28.05	22.5	72.1	129.3	172.4	8.6	73.4					28.05	reporting
29.05	21.9	70.9	140.6	184.5	8.8	73.3					29.05
30.05	12.9	92.8	142.9	184.9	8.4	74.4					30.05	equation

). Once the treatment date has been set, it is necessary to inspect the holding, assess the need for chemical pest control and choose whether to apply the treatment on a case-by-case basis, i.e., separately for each holding, with due account taken of the economic harm threshold, which is:

• 1–2 larvae per blade of winter wheat, winter triticale or rye,
• 1 larva for every 2–3 blades of winter and spring barley, spring wheat, spring triticale or oat.

In 2013, measures were taken to control cereal leaf beetles in winter wheat in Baborówko, the Wielkopolska Region (

Table 6. Treatment date set for Baborówko in 2013 using multiple regression equation based on egg incubation time for both Oulema spp. species.

Date	Average Daily Air Temperature	Average Daily Air Moisture	x1 Total Effective Temperature + 5 Days	x2 Average Effective Temperature	x3 Average Air Moisture	ɛ	Date	Treatment Date Based on:
21.05	14.4	59	63.6	3.8	59.0	3.43	21.05
22.05	12.7	67	54.3	3.0	63.0	2.49	22.05
23.05	9.5	62	47.5	2.0	62.7	1.96	23.05
24.05	11.3	63	40.1	1.7	62.8	1.17	24.05
25.05	9.3	86	35.6	1.3	67.4	0.40	25.05
26.05	9.2	78	43.3	1.1	69.2	−0.26	26.05	+ 5 days
27.05	11.8	71	57.6	1.1	69.4	−1.10	27.05
28.05							28.05
29.05							29.05	reporting
30.05							30.05
31.05							31.05	equations

). The beetles were first seen arriving during stem formation on April 26, while the first larvae broods were discovered during leaf sheath thickening. Following that time, daily records were kept of average temperature and air humidity, both of which are vital for timing pest control treatments. On day six of the development of mass-laid cereal leaf beetle eggs, i.e., on May 26, ε reached −0.26. After the predicted five days were added to the date of May 26, the date of May 31 was suggested for the treatment, which should coincide with the mass hatching of larvae from the mass-laid eggs. The actual mass hatching of larvae was observed between May 29 and June 6. The multiple regression equation predicted ε to be at its lowest on 26 May. Once the 5 predicted days were added, the treatment date was set to be May 31.

4. Discussion

Agriculture is vital for food security and global prosperity. The 2018 OECD [20] report “Concentration in Seed Markets” shows that agriculture currently faces the challenge of increasing productivity while ensuring sustainable development and improving crop resilience. One way to meet such challenges is to foster and apply innovation in agriculture. To that end, use may be made of decision support systems in plant protection. Demand for such systems also results from the requirements and recommendations of integrated management. Integrated pest management principles, in force throughout the EU since 1 January 2014 (Directive 2009/128/EC of the European Parliament and of the Council of 21 October 2009 establishing a framework for Community action to achieve the sustainable use of pesticides) [21] clearly indicate the need to use all available measures for plant protection, with priority given to non-chemical methods. In addition, the Directive states that harmful organisms must be monitored by adequate methods and tools, where available. Such adequate tools should include observations in the field as well as scientifically sound forecasting and early diagnosis systems. In addition, the agricultural revolution propelled by the European Green Deal action plan introduced by the European Commission, and the farm-to-fork strategy and biodiversity strategies, assumes a departure from the use of chemical plant protection in favor of plants’ natural genetic resistance. Adopted by the European Commission on 20 May 2020, such strategies require the Member States to halve the use of plant protection products by 2030 in favor of biological methods and pathogen resistant and tolerant varieties.

While sustainable agriculture systems do not allow for abandoning all plant protection products, such agents must be used responsibly and wisely, i.e., in an environmentally friendly manner that is economically viable for producers and takes into account, especially nowadays, such social factors as rapidly growing human populations and limited farmland and water supplies. In order for cereal producers to be able to apply plant protection products wisely and appropriately in keeping with good plant protection practices, they need not only access to all innovations in plant protection products, but also the latest research findings, not least in forecasting, and especially those concerned with the optimal timing of chemical control of harmful pests. Best-timed treatments mitigate damage risk and prevent excessive and unnecessary use of chemicals.

All studies on warning and consultancy systems as well as systems designed to support decisions to perform chemical treatments need to be based on solid and reliable science. Testing should focus on pest development and how it is affected by the weather. To reduce pest harmfulness, it is essential not only to select appropriate plant protection products but also to adequately time their application. The timing of chemical treatments does more for effective disease and pest control than the choice of a dose or active ingredient in the control agent. Models for forecasting and reporting the need for treatments tailor-made for individual crop pests in view of regional factors (such as the microclimate, seasonal fluctuations in the development of target pests in a given area and differences in “the harmfulness pressure” on individual regions) are critical parts of Decision Support Systems (DSSs).

National agricultural advisory services in several European Member States offer access to DSSs as an integral part of their customer support. Examples of such DSSs are AgrarCommander in Austria [22], MarkOnline in Denmark [23], Mesp@rcelles in France [24], NMP Online in Ireland [25], Web Module Düngung in Germany [26].

DSSs enable advisers and farmers alike to access information on the weather, total effective temperatures, direct phenological observations, permanent and periodic reports of successive pest development stages, pheromone trap catches and recommended plant protection products, and more. With this in mind, research on how to best time chemical treatments is vital for achieving compliance with integrated protection principles, primarily with respect to the proper and timely performance of chemical treatments. Such research requires extended observations in controlled and field environments in conjunction with meteorology. They enable short-term forecasting of pest occurrence to identify periods conducive to the spread of disease or pest reaching development stages, which is when pest control becomes most effective.

As noted by many authors [27,28,29], consultancy models and systems work best in the geographic areas for which they have been designed. Given that many of the existing systems cannot be applied in Poland, it is reasonable to conduct research relevant to the specific microclimatic conditions of this country. Among other things, such research focuses on formulating regression equations that constitute important parts of DSSs. Its aim is to support the optimal timing of treatments for the chemical control of economically important pests.

5. Conclusions

Based on the analyses carried out, it can be concluded that DSS tools should be designed in such a way that they are easy to use, fit into existing user workflows and are trustworthy, which should be developed among users. The use of DSSs and their increasing sophistication and uptake, however, the lack of involvement of end users in the design from the beginning of the process contributes to the fact that DSS tools in agriculture are still very low.