In this article we will discuss about:- 1. Meaning of Pest Forecasting 2. Types of Pest Forecasting 3. Methods.
Meaning of Pest Forecasting:
Pest forecasting is the perception of future activity of biotic agents, which would adversely affect crop production. In other words, it is the prediction of severity of pest population which can cause economic damage to the crop. The systematically recorded data on pest population or damage over a long period of time along with other variable factors, which affect the development of pest, may be helpful in forecasting the pest incidence.
The prediction of a particular pest depends upon characteristics/biology of a pest and the meteorological factors. These meteorological factors may affect the pest either directly affecting their survival, development, reproduction, emergence and behaviour, or indirectly by their action on host plants or on natural enemies. These factors also determine the geographical limits of distribution and the time of appearance and abundance of pests.
The forecasting of pests guides the farmers about the timing and biology of insect incidence, and to eliminate blanket applications, reduce pesticide amounts, and achieve quality results. The farmers can take to timely action of applying various pest control measures to harvest maximum returns.
Several studies are required to generate the basic information, which is required to develop forecasting models.
Some of them are described below:
(i) Quantitative Seasonal Studies:
Using appropriate sampling techniques, the pest abundance must be studied over several years along with seasonal range, variability in number and distribution. The seasonal counts in relation to climate and topography need to be provided.
(ii) Life-History Studies:
The detailed bio-ecology of pest under a range of temperature, humidity, etc. should be known. The duration of different instars, number of generations, survival rate, amount of food eaten, overwintering, host range, number of eggs laid, etc. and other parameters can be studied in laboratory.
(iii) Ecological Studies:
Life-table studies of pest are important for better understanding of pest population build-up, natural mortality factors, intrinsic growth rate, etc. Life-table of a pest can be helpful in finding mating and emergence period which are quite useful for predicting population dynamics of the pest. The migration and immigration of pests can also be used for forecasting of pests.
(iv) Field Studies:
Climatic factors not only affect the pest abundance but also affect the natural enemy population which is an important natural factor in controlling pest population. In field situations, the natural enemy abundance under a range of temperature and humidity should be studied. The other cultural practices like fertilizer application, irrigation, plant spacing, etc., affect the crop phenology which directly influences the population build-up of a pest.
Types of Pest Forecasting:
Pest forecasting may be divided into two categories, viz., short-term forecasting and long-term forecasting.
1. Short-Term Forecasting:
The short-term forecasts are often based on current or recent past conditions that form a basis for, or an enhancement to, the forecast. These may cover a particular season or one or two successive seasons only. The pest population is sampled from a particular area within a crop using appropriate sampling technique and the relationship is established between weather data and progress in pest infestation.
The laboratory studies on the effect of temperature on emergence and egg laying can be used to forecast the pest situation in the field. The short term forecasting can be completely empirical, such as use of environmental cues reported from Japan, where the date of first blooming of cherry blossom and the mean March temperature were used to predict the peak emergence of rice stem borer.
Based on multiple regressions, short term forecasting of wheat grain aphid, Sitobion avenae (Fabricius), has been done. The peak population density on each field was positively correlated with the population densities at the end of ear emergence, mid-anthesis and the end of anthesis. Based on two counts on the crop, the accuracy increased from ear emergence to the end of anthesis, however, the forecast at mid-anthesis of peak density was much more accurate.
2. Long-Term Forecasting:
These forecasts are based on possible effect of weather on the pest population and cover a large area. The data are recorded over a number of years on wide seasonal range and from different areas. Long-term forecasting is based on knowledge of the major aspects of the pest insect’s life- cycle, and of how it is regulated.
The data recorded are analyzed and models are developed based on the available information. The models help in forecasting pest population in various geographical areas based on common weather parameters. Long-term population forecast based on Markov chain theory was developed for effective management strategies for Nilaparvata lugens (Stal) and Sogatella furcifera (Horvath).
A transition probability matrix of 5-yr steps of Markov chain theory was constructed based on 31-yr light-trapping data of the two pests from 1977 to 2007 in Jiangkou County, Guizhou, China. The models accurately forecasted field occurrence in 2008 in Jinangkou County for both species.
This model is an effective method for long-term population forecasting of N. lugens and S. furcifera, and thus provides plant protection agencies and organizations with valuable information in implementing appropriate management strategies.
Long-term forecasting of brown and white backed planthoppers in Japan was based on the assumption that both the hoppers overwinter as diapausing eggs on winter grasses. After it was discovered that the brown planthopper migrates in Japan from outside, the short-term forecasting was adopted.
In China, long-term forecasting of the peak day of immigration, adult numbers caught on the peak day and accumulated adult numbers in the immigrant generation of rice leaf roller, Cnaphalocrocis medinalis Guenee, was done, one year ahead of practical occurrence.
In Kenya, data of 23 years of rainfall was correlated with the number of outbreaks of armyworm, Spodoptera exempta (Walker), in the following season. Based on these data, local or countrywide forecasting of the outbreaks of this pest was done.
Methods for Pest Forecasting:
Pest forecasting has generally based on environmental factors, climatic areas and empirical observations.
1. Environmental Factors:
The population development of a particular pest mainly depends on the favourable environmental conditions available in a particular geographical region. The pest attack occurs in epidemic form only when the favourable environmental conditions for multiplication of pest prevail for longer duration. Therefore, the factors responsible for environmental conditions are the major criteria on the basis of which the forecasting can be done.
The sugarcane pyrilla, Pyrilla perpusilta (Walker), outbreak is predicted based on high temperature during monsoon. The population per 30 plants (Y) is predicted based on the mean maximum temperature (X) of week preceding the data of observation of field population.
It is given as:
Y = (3.47536 × 10-28) (33.4965)x. (0.9552)x
Every insect requires a consistent amount of heat accumulation to reach certain life stages, such as egg hatch or adult flight, which can be interpreted in terms of degree days. One degree day is an accumulation of heat units above some threshold temperature for a 24 hour period. Degree days (often referred to as “growing degree days”) are accurate because insects have a predictable development pattern based on heat accumulation.
Insects are exothermic (cold-blooded) and their body temperature and growth are affected by their surrounding temperature. Biological development of insects over time in correlation to accumulated degree days has been studied, discovering information on key physiological events, such as egg hatch, adult flight, etc.
There is a threshold temperature for each insect; for example, 48°F for the alfalfa weevil, Hypera postica (Gyllenhal). No development occurs when temperatures are below that level. Insects have an optimum temperature range in which they will grow rapidly. Then, there is maximum temperature (termed upper cutoff) above which development stops.
These values can be used in predicting insect activity and appearance of symptoms during the growing season. Therefore, the degree days would be useful in pest management programme to time the scouting of insect pests. This predictive information is known as an insect model. Models have been developed for a number of insect pests.
Degree days = Maximum temperature + Minimum temperature/2 – Development threshold
As an example, codling moth, Cydia pomonella (Linnaeus), pheromone monitoring traps are placed in the apple orchard at 100 degree days after March 1 in northern Utah to determine initiation of adult moth flight.
A temperature range of 50° to 85°F is most comfortable for European corn borer, Ostrinia nubilalis (Hubner). Below 50°F, it will not develop, and above 85°F, development will slow dramatically. A degree day for European corn borer is one of degrees above 50°F over a 24-hour period.
For example, if the average temperature for a 24-hour period was 70°F, then 20 degree-days would have accumulated (70 – 50 = 20) on that day. These accumulations can be used to predict when corn borers will pupate, emerge as adults, lay eggs, and hatch as larvae.
A forecasting model of spring emergence of Carposina sasakii Matsumura in apple orchards in Korea was constructed based on degree-days. The two peaks for adult spring emergence were recorded, first major peak in late June and the second smaller peak in late July. A bimodal distribution model was developed to describe this emergence pattern. The bimodal model predicted more accurately C. sasakii spring emergence times than the Weibull model.
The generation time of C. pomonella populations was predicted using a degree day model. The model was developed after studying 176 generations in walnuts, apples, pears, and other hosts at several locations throughout California. Out of five models, a degree-day model using 10°C as the lower threshold, 31.1°C as the upper threshold (horizontal cutoff), and a generation time of 619 DD provided an adequate fit.
In Japan, three generations of rice leaf roller, recently emerged as serious pest, were recorded in each season. It was estimated that the second generation requires 210 degree-days for development, whereas third generation requires 300 degree-days. The number of degree-days required for development of many insect pests is known in several different countries like Canada, USA, Japan and Europe.
Forecasting of pest emergence, epidemics, etc. has also been reported based on other environmental factors like humidity, rainfall and sunlight. In Tanzania, outbreaks of red locust, Nomadacris septemfasciata (Serville), have been forecast from an index of the previous year’s rainfall.
Severity of Spodoptera exempta (Walker) outbreak seasons was highly dependent upon the number of rainy days during November in central Tanzania. In China, the econometric analyses showed that rise and fall of mirids are largely related to local temperature and rainfall.
2. Observations of Climatic Areas:
The distribution of insects throughout the world is based on evolutionary history which includes main important factor, i.e., climate of the geographical region. There are three distinct zones of abundance of each insect species.
Zone of Natural Abundance (Endemic):
In this zone, the pest species is often in large number, regularly breeds and is a regular pest of some importance. The climate conditions are most favourable for its development and pest is seen all the time.
Zone of Occasional Abundance:
The insect species emerge in epidemic occasionally in this zone because the climatic conditions are either less suitable or the suitable conditions exist only for a short period of time followed by unsuitable conditions. Sometimes, the climatic is severe to destroy the entire population, which is then re-established by dispersal from zone of natural abundance.
Zone of Possible Abundance:
The pest species in this zone can be seen only after migration from zone of natural and occasional abundance outbreaks. The climatic conditions are drastic for their breeding and development. The population is destroyed by the severe climatic conditions within a short period of time. Three different regions Orlando, Naples and Ankara corresponding to zone of natural abundance, occasional abundance and possible abundance, respectively are known for Mediterranean fruit fly, Ceratitis capitata (Wiedemann).
The observation on the climatic areas where critical infestations are likely to occur can be predicted for some insects. Combination of climatic factors like temperature, rainfall, humidity, etc. existing in a geographical region gives an indication of possibility of establishment of pest in that region. The other factors like biotic and topography may also be used for prediction of insect pests.
3. Empirical Observations:
This type of pest forecasting is based on estimating the number of insects available during a particular time. In other words, it is nothing but the sampling of insect or monitoring of pest population. It involves forecasting the population in the next season by counting the pest in the previous seasons. In many cases, the number of pests in the early part of cropping season will give an indication as to the extent of its likely multiplication in the season.
From the counting of immature stages of insects, approximate estimations of later stages can be made. For example, in UK taking soil cores for insect eggs of carrot fly, Psila rosae (Fabricius) and cabbage root fly, Delia radicum (Linnaeus), is successful for estimating the later population of root maggots. The adult catch in the traps especially pheromone traps can be used to estimate the approximate abundance of pest population later in the season.
The sampling of insect pest on alternate host/weeds during non-availability of main crop can be quite useful to forecast the pest population development in the coming season, e.g., counting overwintering eggs of blackbean aphid, Aphis fabae Scopoli, on spindle trees helps in estimating the aphid population on peach-potato crop. In many lepidopteran species, pest forecasting is based on estimating the number of eggs and young larvae on the crop, e.g., cotton bollworms, stem borers, pulse moths, etc.