FORECASTING TECHNIQUES IN CROPS

Transcription

1 FORECASTING TECHNIQUES IN CROPS Ranjana Agrawal I,A.S.R.I., Library Avenue, New Delhi 0 02 ranjana@iasri.res.in Introduction Reliable and tiely forecasts provide iportant and useful input for proper, foresighted and infored planning, ore so, in agriculture which is full of uncertainties. Agriculture now-adays has becoe highly input and cost intensive. Under the changed scenario today, forecasting of various aspects relating to agriculture are becoing essential. But in-spite of strong need for reliable and tiely forecasts, the current status is far fro satisfactory. For ost of the sectors, there is no organized syste of forecasting. The official forecasts (advance estiates) of ajor cereal and coercial crops are issued by Directorate of Econoics and Statistics. Soe work has been attepted in fisheries, livestock, arket projections, etc. by various organizations but these are ostly at research stage. Details of forecasting techniques in crops is discussed here. In crops, production and attack of pests and diseases are the two ajor aspects which need attention. Forecasts of crop production before harvest are required for various policy decisions relating to storage, distribution, pricing, arketing, iport-export, etc. Pests and diseases are one of the ajor causes of reduction in crop yield. Tiely application of reedial easures ay reduce the yield loss. For application of these easures one ust have prior knowledge of the tie and severity of the outbreak of pests and diseases. Forecasting syste can help in this direction.. Forecasting Techniques in Crop Production The advance estiates provided by Directorate of Econoics and Statistics are subjective. There is, thus, a need to develop sound objective forecasts of crop production. Various organisations in India and abroad are engaged in developing ethodology for pre-harvest forecast of crop yields / production using various approaches. The ain factors affecting crop yield / production are inputs and weather. Use of these factors fors one approach for forecasting crop production. The other approach uses plant vigour easured through plant characters. It can be assued that plant characters are integrated effects of all the factors affecting production. Yet another approach is easureent of crop vigour through reotely sensed data. Since area under the crop is available in advance of harvest through Tiely Reporting Schee (TRS) and reotely sensed data, the proble ainly reduces to forecast of yield rate.. Yield forecast using weather paraeters Weather affects crop differently during different stages of crop growth. Thus extent of weather influence on crop yield depends not only on the agnitude of weather variables but also on the distribution pattern of weather over the crop season which, as such, calls for the necessity of dividing the whole crop season into fine intervals. This will increase nuber of variables in the odel and in turn a large nuber of paraeters will have to be evaluated fro the data. This will require a long series of data for precise estiation of the paraeters which ay not be available in practice. Thus, a technique based on relatively saller nuber

2 of anageable paraeters and at the sae tie taking care of entire weather distribution ay solve the proble. Fisher (924) has suggested technique which requires sall nuber of paraeters to be estiated while taking care of distribution pattern of weather over the crop season. He assued that the effect of change in weather variable on crop in successive weeks would not be an abrupt or erratic change but an orderly one that follows soe atheatical law. He assued that these effects are coposed of the ters of a polynoial function of tie. Further, the value of weather variable in w-th week, X w was also expressed in ters of orthogonal functions of tie. Substituting these in usual regression equation Y = A 0 + A X + A 2 X A n X n (here Y denoted yield and X w rainfall in w-th week w =,2,...,n) and utilising the properties of orthogonal and noralised functions, he obtained Y = A 0 + a 0 ρ 0 + a ρ + a 2 ρ a k ρ k where A 0,a 0,a,a 2,...a k are constants to be deterined and ρ i (i=, k) are distribution constants of X w. Fisher has suggested to use k = 5 for ost of the practical situations. In fitting this equation for k = 5, the nuber of constants to be evaluated will reain 7, no atter how finely growing season is divided. This odel was used by Fisher for studying the influence of rainfall on the yield of wheat. Hendricks and Scholl (943) have odified Fisher's technique. They divided the crop season into n weekly intervals and have assued that a second degree polynoial in week nuber would be sufficiently flexible to express the relationship. Under this assuption, the odel was obtained as Y = A 0 + a 0 Σ w X w + a Σ w w X w + a 2 Σ w w²x w In this odel nuber of constants to be deterined reduces to 4, irrespective of n. This odel was extended for two weather variables to study joint effects. Since the data for such studies extended over a long period of years, an additional variate T representing the year was included to ake allowance for tie trend. Another iportant contribution in this field is by Baier (977). He has classified the crop-weather odels in three basic types.. Crop growth siulation odels 2. Crop-weather Analysis odels 3. Epirical statistical odels The ost coonly used odels in crop forecasting are Epirical Statistical odels. In this approach, one or several variables (representing weather or cliate, soil characteristics or a tie trend) are related to crop responses such as yield. The weighting coefficients in these equations are by necessity obtained in an epirical anner using standard statistical procedures, such as ulti-variable regression analysis. Several Epirical Statistical odels were developed all over the world. The independent variables included weather variables, agroeteorological variables, soil characteristics or soe suitably derived indices of these variables. Water V-28

3 Requireent Satisfaction Index (WRSI), Theral Interception Rate Index (TIR), Growing Degree Days (GDD) are soe agrocliatic indices used in odels. Southern Oscillation Index (SOI) has also been used with other weather variables to forecast crop yield (Raakrishna et al. 2003). To account for the technological changes year variable or soe suitable function of tie trend was used in the odels. Soe workers have also used two tie trends. Moving averages of yield were also used to depict the technological changes. In contrast to epirical regression odels, the Joint Agricultural Weather Inforation Centre eploys the crop weather analysis odels that siulate accuulated crop responses to selected agroeteorological variables as a function of crop phenology. Observed weather data and derived agroeteorological variables are used as input data. USDA and FAO are the two organisations that systeatically forecast world agricultural production and global crop inforation based on weather. Daily onitoring of satellite weather iages and eteorological data provides the fraework for agricultural weather analysis. Daily, weekly and seasonal suaries are processed and erged with historical weather and crop data for evaluation of the crop yield potential. FAO has also carried out nuber of studies using agro eteorological odels. The ethodology consists of developing an index depending on water deficit / water surplus in successive periods of crop growth. These odels have good potential for early crop yield assessent for rainfed crops. (Frere and Popov. 979). In India, ajor organisations involved in developing ethodology for forecasting crop yield based on weather paraeters are IMD and IASRI. The ethodology adopted by IMD involves identification of significant correlations between yield and weather factors during successive overlapping periods of 7 to 60 days of the crop growing season. By analysing the correlation coefficients for statistical and phenological significance, the critical periods when the weather paraeters have significant effect on yield are identified. The weather paraeters in critical periods alongwith trend variables are used through ultiple regression analysis to obtain forecast equations. Using this ethodology odels were developed for principal crops on eteorological subdivisions basis. Data fro various locations are averaged to get the figures for eteorological sub-divisions and these are utilised to develop the forecast odel. Monthly forecasts are issued fro these odels by taking the actual data upto tie of forecast and noral for the reaining period. In soe odels yield Moisture Index, Generalised Monsoon Index, Moisture Stress, aridity anoaly Index are also used (Sarwade, 988; Sarkar, 2002). At IASRI, the odel suggested by Hendricks and Scholl has been odified by expressing effects of changes in weather variables on yield in the w-th week as function of respective correlation coefficients between yield and weather variables. This will explain the relationship in a better way as it gives appropriate weightage to different periods. Under this assuption, the odels were developed for studying the effects of weather variables on yield using coplete crop season data whereas forecast odel utilised partial crop season data. These odels were found to be better than the one suggested by Hendricks and Scholl. The forecast odel finally recoended was of the for V-29

4 p p Y= A0 + aij Zij + aii' j Zii' j+ ct + e i= j= 0 i i' = j= 0 Where Z ij = w= r j iw X iw and Z ii'j = r j ii'w X iw X i'w w= Here Y is yield, r iw /r ii'w is correlation coefficient of yield (adjusted for trend effect) with i-th weather variable (X iw ) /product of i-th and i'-th weather (X iw X i w ) variables in w-th week, is week of forecast, p is nuber of weather variables used and e is error ter. Models were successfully used for forecasting yields of various crops at district level as well as agrocliatic zone level. (Agrawal et al 980; 983, 986, 200; Jain et al 980; Mehta, et al. 2000). These odels were used to forecast yield of paddy and wheat in different situations, viz (i) rainfed area having deficient rainfall (paddy), (ii) rainfed area having adequate rainfall (paddy) and (iii) irrigated area (wheat). The results revealed that reliable forecasts can be obtained using this approach when the crops are 0-2 weeks old. This approach was also used to develop forecast odel for sugarcane at district level (Mehta, et al. 2000). However, these studies were carried out at district level and required a long series data of years which are not available for ost of the locations. Therefore, the study has been undertaken to develop the odel on agro-cliatic zone basis by cobining the data of various districts within the zone so that a long series could be obtained in a relatively shorter period. Previous years yield, oving averages of yield and agricultural inputs were taken as the variables taking care of variation between districts within the zone. Year variable was included to take care of technological changes. Different strategies for pooling district level data for the zone were adopted. Results revealed that reliable forecasts can be obtained using this ethodology at 2 weeks after sowing i.e. about 2 onths before harvest. The data requireent reduced to 0-5 years as against years for district level odels. The approach has been successfully used for forecasting yields of rice, wheat and sugarcane for Uttar Pradesh. (Agrawal et al. 200). At district level, odel based on tie series data on weather paraeters has also been developed using technique of discriinant function analysis. The long series of years has been classified into three groups congenial, noral and adverse with respect to crop yields. Using weather data of these groups, linear / quadratic discriinant functions were fitted. These functions were used to find weather scores for each year at different phases of crop growth and were used as regressors in forecast odel. (Rai, et al. 2000). In another approach based on water balanced technique, odels for rainfed crops using weighted stress indices have been developed. In this approach, water deficit / surplus has been worked out at different phases of crop growth and using suitable weights, accuulated weighted stress index has been developed for each year which was used as regressor in the forecast odel. (Saksena et al. 200). V-30

5 .2 Yield forecast based on plant characters Effects of weather and inputs are anifested through crop stand, nuber of tillers, leaf area, nuber of earheads etc. which ultiately deterine crop yield. As such, plant characters can be taken as the integrated effects of various weather paraeters and crop inputs. Thus the other approach to forecast crop yield is to use plant characters. In USDA, the net yield per acre for each saple plot is coputed as (Vogel, 985). y i = (F i x C i x W i ) L i where F i = Nuber of fruits harvested or forecast to be harvested in the i-th saple plot. C i = Conversion factor using the row space easureent to inflate the plot counts to a per acre basis. W i = Average weight of fruit harvested or forecast to be harvested. L i = Harvest loss as easured fro post-harvest gleanings (the historic average is used during the forecast season). y i = ( Σ n ) for the n saple plots. y i Separate odels are used to forecast the nuber of fruits (F i ) to be harvested and the final head weight (W i ). This ethod cannot be followed in India/tropical countries as tie period fro head eergence to aturity is hardly one to two onths for ost of the crops whereas in USA this takes two to three onths. Forecast of head weight at aturity therefore cannot be obtained uch in advance in India, as such this will not be useful for obtaining early forecast in such countries. In India, yield is directly regressed on plant counts and yield contributing characters for obtaining forecast odel. Considerable work has been done at IASRI using this approach. The data are collected at different periodic intervals through suitable sapling design for 3 to 4 years fro farers fields. Two types of approaches have been attepted - Between year odel and Within year odel..2. Between year odels These odels are developed taking previous year(s) data. Objective yield forecasts are obtained by substituting the current year plant data into a odel developed fro the previous year(s). An assuption is ade that the present year is a part of the coposite population of these (previous) years. Most coonly used odels are based on regression approach. Different Between year Models Linear regression odels Y = ß 0 + ß X + ß 2 X e where Y and X i are yield and plant characters respectively. These ay be used in original scale or soe suitably transfored variables of these can be used. ß 0 and ß i are constants to be estiated and e is rando error. These odels utilise data at one point of tie only during the crop growth. (Sardana et al. 972, Singh et al. 976, Jha et al. 98, Singh et al. 988). V-3

6 These odels were iproved taking regressors as priniciple coponents of plant charcters (Jain et al. 984) or growth indices based on plant characters observed on two or ore points of tie during the crop growth (Jain et al 985). The growth indices are obtained as weighted accuulations of observations on plant characters in different periods, weights being respective correlation coefficients between yield and plant characters. The odel can be written as Y = ß 0 + ß i G i + e where G i = n 2 w= n r iw X iw G i is the index of the i-th character, w is period identification, n & n 2 are the initial and final periods considered in developing the index of the character, r iw is siple/partial correlation coefficient between yield and i-th character in w-th period (X iw ). Probability odel Multiple regression technique has been extensively used in developing odels for crop yield forecasting. Least squares technique is used for estiating the paraeters of the regression odel. The optiality properties of these estiates are described in an ideal setting which is not often realised in practice. It has been observed that regression based on different subsets of data produce very different results, raising questions of odel stability. To overcoe soe of the drawbacks of regression odel probability odel for forecasting crop yield using Markov Chain theory has been developed. This ethod, being copletely odel free, does not require any assuption about independent and dependent variables. Markov Chain ethod has the advantage of providing non-paraetric interval estiates and is robust against outliers/extree values. In this ethod, growth process of the crop is divided in s phenological stages. A arkov chain odel is constructed by defining a set of states, which describe the condition of an individual plant (or average condition of a group of plants) at specified tie within the phenological stages. Individual states are defined on the basis of available qualitative and quantitative inforation to describe plant condition. Let n i, for i =,2,...,s denote the nuber of states at the coenceent of stage i. Let A i,i+ for i =,2,...,(s-) denote the (n i x n i+ ) transition atrix which gives the transition probabilities of a plant (or group of plants) oving fro any possible state of stage i to any possible state of stage i+. As a property of transition atrices, each row of an A i,i+ atrix sus to. Let F denote the atrix of transition probabilities fro each of the (n-n s ) states of (s-) interediate stages to each of the n s states, the last (harvest) stage. The n s states are defined as quantitative intervals of yield. F atrix can be obtained as s π Ai, i+ i= s F = π Ai, i+ i= 2 As 2, s A As, s s, s V-32

7 F atrix can be used to forecast crop yields. Each row of F represents a crop condition (state) at a certain crop stage. The n s states of the final stage are defined as quantitative intervals of yield. Each colun of F represents a different yield interval. The values of each row of F are the estiated probabilities of the crop producing a final yield within each of the n s intervals. Thus, each row of F is a predicted yield distribution for a given stage and state. Each of the (n-n s ) forecast yield distributions in the F atrix ay be analysed to get ean and standard error of the forecast. In particular, transition probability atrix A s-,s will give ean and standard error of forecast at stage(s-). This ethod was applied to forecast yield of corn and cotton by USDA (Matis et al. 985, 989) and sugarcane (Jain and Agrawal 992(a); Agrawal and Jain 996). Models using higher order arkov chain and using principal coponents and growth indices of plant characters in arkov chain approach were also developed. (Jain and Raasubraanian,998 ; Raasubraanian and Jain 999; Raasubraanian, et al. 2004)..2.2 Within year odels The 'between year odels' while perforing satisfactorily in typical years ay falter in atypical years. A odel which uses data fro the current growing season only ay be beneficial in iproving forecasts during a year with atypical growing conditions. These odels are developed to provide forecasts of pertinent coponents of crop yield relying entirely on growth data collected fro plant observations during the current growing season. A logistic odel having soe yield coponents as dependent variable and an independent 'tie' variable generally fits well to the growth process of crop yield coponents like dry atter accuulation etc. The odel uses repeated observations fro the current year to estiate the paraeters needed to forecast the dependent variable at aturity. The odel is Y i = α / (+ß ρ ti ) + e i i =,2,...,n; α > 0, ß > 0, 0 < ρ < where Y i = dependent growth variable t i = independent tie variable e i = error ter Partial crop season data are utilised to fit the curve and the value at harvest is predicted through this curve which in turn is used to forecast yield. (Nealon 976, House 977, Larsen 978, Jain et al. 992(b)). The paraeter α is the ost iportant paraeter to be estiated as it gives average aount of yield coponent (eg. dry atter) at aturity. It is likely to be over estiated when partial crop season data based on sall data points that too falling on the lower side of the curve where the growth has steep rise are used to fit the odel to forecast the yield coponent at aturity. This ay need suitable odification in the odel so as to capture α (dry atter at aturity) fro partial crop season data. The odified logistic odel (Jain et al 992(b)) is as follows: Yi = t t f α ( + βρ ti ) + e where t is tie of aturity and t f is the tie of forecast. V-33

8 .3 Models using spectral data Since the approach using plant characters requires collection of data fro farers' fields, the data can be used on characters which can be easured easily without involving uch expertise, cost and sophisticated instruents. Soe characters contributing significantly towards yield ay not find place in the odel due to these liitations. This calls for the necessity of including soe other variables in the odel alongwith bioetrical characters which could take care of such variables indirectly. During the last three decades, considerable work has been carried out in India in the spectral response and yield relationships of different crops at Space Applications Centre, Ahedabad, under the reote sensing applications ission called Crop Acreage and Production Estiation (CAPE). Spectral indices such as ratio of infra red (IR)/Red(R) and Noralised difference (ND) = (IR-R) / (IR+R) are calculated fro reotely sensed data and are used as regressors in the odel (SAC report 990). The schee needed further iproveent. Project has been forulated to integrate Agroeteorology and Land-based observations alongwith reote sensing data. Project title is Forecasting Agricultural outputs using Space, Agroeteorology and Land-based observations (FASAL). The experience in this context is that reote sensing can suppleent the existing data collection syste but never copletely replace it. The two data collection systes ust be integrated through rigorous statistical ethodology. At Space Application Centre, ethodology has been developed which provides ultiple forecasts for rice and wheat using reotely sensed data for acreage forecast whereas forecasts for productivity are obtained using eteorological and agroeteorological indices. (Patel et al. 2004).4 Models using Farers' Appraisal Farer is the best judge of the likely production in the field. Farers' appraisal, therefore, could serve as a good input for forecasting the yield and replace soe of the characters requiring expertise or use of sophisticated instruents for their easureents and thus reducing the cost on data collection. A study has been carried out to study the feasibility of using farers' appraisal in the forecast odel for sugarcane. (Agrawal and Jain, 996). The results revealed that a reliable forecast could be obtained using plant population and farers appraisal. Another ethodology based on farers appraisal data has been developed using Bayesian approach. The study has been carried out for wheat in Muzaffarnagar district. Expert opinion data were collected in a nuber of rounds in a year by interviewing the selected farers regarding their assessent about the likely crop production and chance of occurrences in various yield classes. Fro these responses average prior probabilities were coputed. Actual harvest yield and farers appraisal data on yield for previous year(s) were taken into account to obtain posterior probabilities which were then used for obtaining Bayesian forecast of crop yield for current year. (Chandrahas et al. 200) V-34

9 .5 Integrated approach Models using data on plant characters alongwith agricultural inputs were found to be better than odels based on plant characters alone in jowar and apple (Jain et al. 985; Chandrahas and Narain, 992). Often it is not possible to include all the variables in a single odel. In such situations coposite forecast can be obtained as a suitable cobination of forecasts obtained fro different odels. Various strategies for cobining forecasts have been suggested under different situations. (S.C.Mehta, 2000 ). 2. Forewarning Syste for Crop Pests and Diseases In pests and diseases forewarning syste, the variables of interest could be axiu pest population / disease severity, pest population / disease severity at ost daaging stage of the crop, pest population / disease severity at different stages of crop growth or at various standard weeks, tie of first appearance of pests / diseases, tie of axiu pest population / disease severity, tie of pest population / disease severity crossing threshold liit, extent of daage, weekly onitoring of pests and diseases progress, occurrence / non-occurrence of pests and diseases. If data are available at periodic interval for 5-20 years, the detailed study can be carried out for different paraeters. However, depending upon the data availability, different types of odels can be utilized for developing forewarning syste. As in the case of yield forecasts, for pest and disease forewarning also the odels could be of two types, 'Between year odel' and 'Within year odel'. 2. Between year odels If data are available in quantitative for, the different types of odels used are : Thub rule: This approach is the ost coon and extensively used. It is a siple syste which describes the forecasting of the pests and diseases based on past experience. For exaple for potato late blight, a day is favorable if - the 5 day teperature average is < C - the total rainfall for the last 0 days is > 3.0 c - the iniu teperature on that day is > C Regression Model: The regression odel taking pest /disease paraeter as dependent variable and suitable independent variables such as weather variables, crop stages, population of natural eneies/predators etc. is used. These variables are used in original scale or on a suitable transfored scale such as cos, log, exponential etc. (Coakley et al 985; Trivedi et al. 999). Model based on weather indices: This is a odel siilar to the one used in yield forecasting. The recoended odels is of the for p p Y = a0 + aizi + biizii' + e i = i i' n2 n2 where Zi = r iw X iw ; Zii' = r ii ' w X iw X i' w w = n w = n V-35

10 Y is variable to forecast; X iw is value of i th weather paraeter in w th period; r iw / r ii'w is value of correlation coefficient between Y and i-th weather paraeter/product of i-th and i'-th weather variable in w th period; p is nuber of weather variables considered; n and n 2 are the initial and final periods for which weather paraeters are included in the odel; e is error ter. (Agrawal, et al. 2004). If inforation on favourable weather conditions is known, subjective weights based on this inforation can be used for constructing weather indices. Principal coponent regression: Forewarning odels can be developed using the principal coponent techniques as norally relevant weather variables are large in nuber and are expected to be highly correlated aong theselves. Using the first few principal coponents of weather variables as independent variables forecast odels can be developed. Discriinant function analysis: The ethodology is siilar to the one used for yield forecasting, replacing yield by the character depicting pests and diseases. (Johnson et al 996) Coplex polynoial [Group Method of Data Handling (GMDH)]: It provides coplex polynoial in independent variables. It selects the structure of the odel itself without prior inforation about relationship. For of the odel : Y = a + i= bi Xi + j= cij Xi X j + i= j= k = dijk Xi X j X k The technique involves fitting of quadratic equations for all pairs of independent variables and identifying a few best perforers in ters of predictive ability (using appropriate statistics); converting entire set of independent variables (called zero generation variables) to new variables (first generation variables) which are obtained as predicted values fro these selected quadratic equations (of zero generation variables). The process of fitting and identifying best quadratic equations is repeated using first generation variables and second generation variables are obtained. The whole process is repeated with every new generation of variables till appropriate odel is obtained (using certain criteria). At final stage, one best quadratic equation is selected as the final odel. (Bahuguna et al 992; Trivedi et al 999). Artificial Neural Network (ANN): ANN provides an attractive alternative tool for forecasting purposes. ANNs are data driven self-adaptive ethods in that there are few apriori assuptions about the odels for probles under study. They learn fro exaples and capture subtle functional relationships aong the data even if the underlying relationships are unknown or hard to describe. After learning the data presented to the, ANNs can often correctly infer the unseen part of a population even if data contains noisy inforation. As forecasting is perfored via prediction of future behaviour (unseen part) fro exaples of past behaviour, it is an ideal application area for ANNs, at least in principle. (Dewolf et al. 997, 2000; Agrawal et al. 2004). However, the technique requires a large data base V-36

11 Deviation Method: This ethod can be utilized when periodical data at different intervals during the crop season are available for only 5-6 years. The pest population at a given point of crop stage is assued to be due to two reasons natural cycle of the pest and weather. To identify the natural cycle, data at different intervals is averaged over years and a suitable odel is fitted to these averaged data points. Then the entire data is adjusted by taking deviations fro this natural cycle. Appropriate odel is the fitted using these deviations for natural cycle as dependent and weather as independent variables. Model for Qualitative data: Soeties quantitative inforation on pests and diseases is not available but is available in qualitative for such as occurrence / non-occurrence, low / ediu / high. In such cases, the data are classified as 0/ (2 categories); 0,,2 (three categories). The logistic regression is used for obtaining probabilities of different categories. For exaple, for two categories, the odel is of the for : P(E =) = + exp( z) where Z is a function of weather variables. Forecast / Prediction rule : If P.5 ore chance of occurrence of epideic If P <.5 probability of occurrence of epideic is iniu (Mishra et al. 2004; Johnson et al. 996; Agrawal, et al. 2004) 2.2 Within year odel Soeties, past data on pests and diseases are not available but the pests and diseases status at different points of tie during the crop season are available. In such situations, within years growth odel can be used for forewarning axiu disease severity / pest population, provided there are 0-2 data points between tie of first appearance of pest / disease and axiu or ost daaging stage. The ethodology is siilar to yield forecast odel as given in section.2.2 (Agrawal et al., 2004). References Agrawal, Ranjana, Jain, R.C., Jha, M.P. and Singh, D. 980: Forecasting of rice yield using cliatic variables. Ind.J.Agri. Sci. 50(9) : Agrawal, Ranjana, Jain, R.C. and Jha, M.P. 983 : Joint effects of weather variables on rice yield, Mausa, 34 (2) : Agrawal, Ranjana, Jain, R.C., Jha, M.P., 986 : Models for studying rice crop-weather relationship, Mausa, 37(): Agrawal, Ranjana and Jain, R.C. 996 : Forecast of sugarcane yield using eye estiate alongwith plant characters. Bioetrical Journal, 38 (5) : Agrawal, Ranjana; Jain, R.C. and Mehta, S.C. (200). Yield forecast based on weather variables and agricultural inputs on agrocliatic zone basis. Indian Journal of Agricultural Science, 7(7). Agrawal, Ranjana, Mehta, S.C., Bhar, L.M. and Kuar, Arender (2004). Developent of weather based forewarning syste for crop pests and diseases - Mission ode project under NATP. V-37

12 Bahuguna, G.N. and Chandrahas (992). Models for forecasting aphid pest of ustard crop. (IASRI Publication). Baier, W. (977). Crop weather odels and their use in yield assessents. Tech. note no. 5, WMO, Geneva, 48 pp. Chandrahas; Narain, Pre (992). Pre-harvest forecasting of yield of apple. (IASRI Publication). Chandrahas, Rai, T. (200). Pilot study for developing Bayesian probability forecast odel based on farers appraisal data on wheat crop. (IASRI publication). Coakley, S.M., Mcdaniel, L.R. and Shaner, G. (985). Models for predicting severity of septoria tritici blotch on winter wheat. Phytopathalogy, 75() : Dewolf, E.D. and Francl, L.J. (997). Neural network that distinguish in period of wheat tan spot in an outdoor environent. Phytopathalogy, 87() : Dewolf, E.D. and Francl, L.J. (2000). Neural network classification of tan spot and stagonespore blotch infection period in wheat field environent. Phytopathalogy, 20(2) :08-3. Fisher, R.A. 924 : The influence of rainfall on the yield of wheat at Rothasted. Roy. Soc. (London), Phil. Trans. Ser.B.,23 : Frere, M. and Popov, G.F., 979 : Agroeteorological crop onitoring and forecasting. FAO plant production and protection paper, plant production and protection division, FAO, Roe. Hendrick, W.A. and Scholl, J.C. 943 : Technique in easuring joint relationship. The joint effects of teperature and precipitation on crop yield. N. Carolina Agric. Exp. Stat. Tech. Bull. 74. House, C.C A within year growth odel approach to forecasting corn yields. Econoics, Statistics and Co-operative Service U.S. Departent of Agriculture, Washington, D.C Jain,R.C., Agrawal, Ranjana and Jha, M.P. (980) : Effects of cliatic variables on rice yield and its forecast, Mausa 3(4) : Jain,R.C. and Agrawal, R. (992(a)). Probability odel for crop yield forecasting. Bioetrical Journal, 34(4) : 50-. Jain, R.C., Agrawal, Ranjana and Singh, K.N. (992(b)). A within year growth odel for crop yield forecasting. Bioetrical Journal, 34(7) : Jain, R.C., Sridharan, H. and Agrawal, R. (984). Principal coponent technique for forecasting of sorghu yield, Indian J. Agric. Sci. 54(6) : Jain, R.C., Jha, M.P. and Agrawal, R. (985). Use of growth indices in yield forecast. Bioetrical Journal, 27(4) : Jain, R.C. and Raasubraanian, V. (998), Forecasting of crop yields using second order Markov Chains. Journal of the Ind. Soc. of Agril. Stats. Vol. LI() : Jha, M.P., Jain, R.C. and Singh,D. (98). Pre-harvest forecasting of sugarcane yield. Indian. J. Agric. Sci. 5(0) : Johnson, D.A., Alldredge, J.R. and Vakoch, D.L. (996) Potato late blight forecasting odels for the sei-arid environent of South-Central Washington. Phytopathalogy, 86(5) : Larsen, Grog. A. (978). Forecasting (977). Kansas wheat growth. Econoics, Statistics and Cooperative Service, U.S. Departent of Agriculture. Washington D.C V-38

13 Matis, J.H., Gaito, T., Grant, W.E., Iwing, W.C. and Ritchie, J.T. (985). A Markov chain approach to crop yield forecasting. Agricultural Systes, 8 : Matis, J.H., Birkett, T. and Boudreaux, D. (989). An application of Markov chain approach to forecasting cotton yield fro surveys. Agricultural Systes, 29 : Mehta, S.C., Agrawal, Ranjana; Singh V.P.N. (2000): Strategies for coposite forecast. JISAS, 53(3). Mishra, A.K., Prakash, O. and Raasubraanian, V. (2004). Forewarning powdery ildew caused by Oidin angiferae in ango (Mangifera indica) using logistic regression odels. Indian Journal of Agricultural Sciences, 74(2) : Nealon, Jack, (976). The developent of within-year forecasting odels for winter wheat. Research and developent branch, Research Division, Statistical Reporting Service, U.S. Departent of Agriculture. Patel, N.K., Chakraborty, M., Dutta, S., Patnaik, S., Parihar, J.S., Moharana, S.C., Das, A., Sarangi, B.K. and Behera, G. (2004). Multiple forecasts of kharif rice in Orissa state - Four year experience of Fasal pilot study. Journal of the Indian Society of Reote Sensing, 32(2) : Rai, T. and Chandrahas (2000). Use of discriinant function of weather paraeters for developing forecast odel for rice crop. (IASRI Publication). Raakrishna, Y.S., Singh, H.P. and Rao, G. Nageswara (2003). Weather based indices for forecasting foodgrain production in India. Jounral of Agroeteorology 5() :. Raasubraanian, V. and Jain, R.C. (999). Use of growth indices in Markov Chain odels for crop yield forecasting. Bioetrical Journal, 4() : Raasubraanian, V., Agrawal, Ranjana; Bhar, L.M. (2004). Forecasting sugarcane yield using ultiple Markov chains. Sardana, M.G., Khosla, R.K., Ohri, N.K., Mitra, P.C. (972). Pre-harvest forecasting of yield rate of jute. Jute Bull. 35 (7 and 8). Sarkar, J. (2002). Forecasting rice and wheat yield over different eteorological sub divisions of India using statistical odels. 56 th Annual Conference of ISAS. Sarwade, G.S. (988). Meteorological yield odels. IRS-UP/SAC/CYM/TN/7/58, SAC, Ahedabad. Saksena, Asha; Jain, R.C., Yadav, R.L. (200). Developent of early warning and yield assessent odel for rainfed crops based on agroeteorological indices. (IASRI publication). Singh, B.H., Bapat, S.R. (988). Pre-harvest forecast odels for prediction of sugarcane yield. Indian J. Agric. Sci. 58(6) : Singh, D., Singh, H.P., Singh, Singh, P. (976). Pre-harvest forecasting of wheat yield. Indian J. Agric. Sci. 46(0) : Space Application Centre, Ahedabad, (990). Review Docuent of RSAM Project : Crop acreage and production estiation. RSAM/SAC/CAPE/RD/27/90 : 25. Trivedi, T.P., Paul Khurana, S.M., Jain, R.C., Mehta, S.C. and Bhar, L. M. (999), Developent of Forewarning syste for aphids (Myzus persicae) on potato, Annual Report NCIPM, N. Delhi. Vogel, Fred. A. (985). Crop forecasting ethodology current and future procedures in the U.S. Proceedings of the 45 th session of the International Statistical Institute, held at Asterda : 2.- to V-39