Disease assessment methods and their use in simulating growth and yield of peanut crops affected by leafspot disease

Transcription

1 Annals of Applied Biology (5), 146: Disease assessment methods and their use in simulating growth and yield of peanut crops affected by leafspot disease M ADOMOU 1, P V V PRASAD *, K J BOOTE and J DETONGNON 1 1 INRAB, BP 884, Cotonou, Republic of Benin Agronomy Department, University of Florida, PO Box 115, Gainesville, FL 3611, USA Summary Peanut (Arachis hypogaea) crops in Benin often experience late leafspot (Cercosporidium personatum), which causes severe yield losses associated with leaf defoliation and necrosis. The objective of this research was to determine the best method of disease assessment and to test its utility in the CROPGRO-peanut model to simulate growth and yield as affected by late leafspot in early and late maturing peanut cultivars grown at different sowing dates under rain-fed conditions (without irrigation) in northern Benin. Two peanut cultivars TS 3-1 and were sown on three dates between May and August during 1998 and In both years there was severe occurrence of late leafspot and the progression of disease was earlier and faster with later sowing dates. Overall, the long duration cultivar produced greater yield than the short duration cultivar TS 3-1. The CROPGRO-peanut model was able to predict and simulate the observed crop and pod dry matter over time when input on percent diseased leaf area and percent defoliation were provided. Of several disease assessments, the best approach was to input measured percent main-stem defoliation above the fourth node and percent diseased leaf area estimated from visual leafspot score. Keywords: Peanut, leafspot, disease assessment, defoliation, yield, crop modeling Introduction Peanut or groundnut (Arachis hypogaea L.) is one of the most important food legume crops and an important source of protein and oil in diets in most of West Africa including Republic of Benin. The average peanut yield in Benin during 3 was 735 kg ha -1, which is lower than the average for Africa (85 kg ha -1 ), West Africa (96 kg ha -1 ), Asia (1619 kg ha -1 ) or the world (1349 kg ha -1 ) (FAO, 3). The lower productivity in West African region is due to lack of improved cultivars, drought stress during the growing season, diseases, low fertility, and poor cultural or agronomic practices used by local farmers (Adomou, 199). Important abiotic factors include temperature extremes, water deficit and low soil fertility, while biotic factors such as occurrence of pests and weeds also cause significant yield losses (Ntare & Waliyar, 1994). In Benin, peanut crops are entirely rain-fed; thus the sowing date is determined by the start of the rainy season. The most common fungal pathogen that causes significant reductions in yield and/or quality of peanut is late leafspot caused by Phaeoisariopsis personata (Berk. & Curtis) Arx = Cercosporidium personatum (Berk. & Curtis) Deighton (Adomou, 199). Severe occurrence of foliar disease can reduce pod yield by 5% to 7% (Waliyar, 1991), and this is a common occurrence because farmers in West Africa in general and Benin in particular do not use fungicides. Yield reductions associated with foliar diseases are related to the loss of leaf area and the resulting reduction in light interception and canopy photosynthesis (Boote et al., 198; Bourgeois & Boote, 199). The date of sowing, weather, cultivar choice and time requirement for maturation of cultivars affect the occurrence of diseases. At present, there is no significant leafspot-resistance in short-season cultivars commercially available for West Africa. The yield-limiting factors are highly locationspecific and depend on soil type, weather conditions, and cultural practices. Identifying yield gaps by field experimentation continues to be difficult, time consuming and cost-prohibitive. Crop growth simulation models have the potential to predict crop performance under varying crop management conditions, and to help applied research and demonstration strategies for improving crop production (Thornton, 1991). However, use of models to evaluate productivity of peanut in this region requires that the model accurately simulate growth and yield as a function of cultivar, sowing date, diseases, and drought conditions. The crop models can then be used to design a package of improved agronomic crop management to improve productivity. The CROPGRO-peanut model is a mechanistic and process-oriented crop growth simulation model (Boote et al., 1998). It includes crop carbon balance, crop and soil N balance, and soil water balance. The model computes canopy photosynthesis at *Corresponding Author vpaga@mail.ifas.ufl.edu; vvpagadala@hotmail.com; Fax: Association of Applied Biologists

2 47 M ADOMOU ET AL. hourly time steps using leaf-level photosynthesis parameters. The model dynamically responds to daily weather inputs (temperature, radiation, rainfall, wind speed and relative humidity), soil water deficit, cultural practices and cultivar choice (Boote et al., 1998). Effects of foliar diseases on peanut growth and yield can be estimated either by coupling the crop simulation models with mechanistic disease simulators or by input of actual values for observed pest damage based on scouting information (Boote et al., 1993). Boote et al. (1983) classified pests into different categories based on the damage caused, and described various coupling points in models which could be used for simulating pest damage. This approach is being used in crop models and has been shown to be valid and useful (Batchelor et al., 1993; Boote et al., 1993). Effects of disease on production were more accurately simulated by inputs of observed damage than with the mechanistic disease simulation approach because the latter mostly depends on weather inputs and has no feedback correction (Boote et al., 1993). It is important to have reliable disease assessment methods which provide accurate scouting data on crop damage for better understanding of disease epidemiology, crop-loss assessments, thus providing inputs to crop models and for development of crop management practices. There are several disease assessment methods for plant diseases including peanut leafspot. Commonly used methods include estimations of main-stem defoliation, total percent defoliation of whole plant and estimations of percent diseased leaf area of the whole plant, part of the plant or individual leaves, measurement of healthy green leaf area (Nutter et al., 1991; Bourgeois et al., 1991; Nutter & Littrell, 1996). Simplified visual disease severity scores based on relative scales (for example 1-9 of Subramanyam et al., 1995; 1 1 of Chiteka et al., 1997) can be used to estimate loss of leaf area and diseased leaf area. The difficulty of these disease scores for crop model input is that they do not necessarily differentiate between loss of leaf area due to defoliation and diseased (necrotic) leaf area, and there are associated problems in translation of these scores into percent defoliation and necrosis. In addition, disease-induced defoliation often complicates scoring during the crop growth period due to difficulty in accounting for cumulative effect of disease over time because of loss (defoliation) of diseased leaves. Thus, it is important to evaluate disease assessment methods as valid scouting inputs to crop models for accounting for leafspot disease effects on yield. The objective of the present research was to determine the best method of disease assessment and to test its utility in CROPGRO-peanut model to simulate growth and yield as affected by late leafspot in early and late maturing peanut cultivars grown at different sowing dates under rain-fed conditions (without irrigation) in northern Benin. Materials and Methods This research was conducted at the Institut National des Recherches Agricoles du Benin (INRAB) research station at Ina (1 N latitude and.95 W longitude), Republic of Benin. Soil samples were collected prior to sowing from different soil horizons and were analysed for particle size distribution, chemical properties and exchangeable and extractable nutrients. Soil texture was loamy sand for the top 4 cm of soil, a sandy loam from 4 48 cm and a sandy clay loam from 48 1 cm. The ph ranged from 6.6 to 6.8. Details of soil properties at the experimental site are given in Table 1. Treatment details Two cultivars, an early-maturing (9 days) Spanish botanical type cv. TS 3-1, and a late-maturing (1 days) Virginia type cv , were sown at three dates during 1998 and Sowing dates were 8 May, June and 16 July in 1998; and June, 1 July and 3 August in The cultivar and sowing Table 1. Soil properties at the experimental site near Ina in northern Benin prior to sowing in 1998 Property of each soil depth (cm) Parameter to to 4 4 to to to 1 ph (1: soil water ) CEC (cmol kg -1 ) Total N (g kg -1 ) Total C (g kg -1 ) Available P (mg kg -1 ) Exchangeable K (cmol kg -1 ) USDA texture class Loamy sand Sandy loam Sandy clay loam

3 Simulating yield reduction due to leafspot in peanut 471 date treatments were randomly allocated to plots of size 6 m 5 m using a row spacing of 5 cm. The design of the experiment was a complete randomised block design with four replicates. Plant husbandry In both years the preceding rainy-season crop in the selected field was maize (Zea mays L.). In each year the soil was thoroughly prepared by ploughing the field to 15 cm depth using tractor-drawn disc plough. The field was fertilised with a broadcast application of phosphorus at the rate of 46 kg ha -1 P O 5 using triple superphosphate, followed by harrowing and levelling. Seeds were sown at a spacing of 5 cm between rows, and 1 cm within the row at a depth of about 5 cm. The seeds were not treated with any chemical nor inoculated with Rhizobium. The crop was not irrigated. Thinning and gap filling (re-sowing) was done at 15 days after sowing (DAS) to maintain uniform plant population. Plots were kept weed free by hand weeding and interrow cultivation using hand hoes. No fungicides or pesticides were applied. There was no serious insect damage, but there was severe occurrence of foliar leafspot disease only. Observations on growth and phenology Durations (days) from sowing to the appearance of first flowers (R1, Boote, 198), pegs (R), and harvest maturity (R8) were recorded for all treatments. During each year, sub-samples of plants were harvested four times from 1 m (-m long single row) at 1 to 15-day intervals from anthesis to maturity to obtain time course growth analysis data. For yield determination, all plants from 1 m were harvested at maturity (between days after sowing for TS-3-1 and 1 and 17 days after sowing for 69-11). At each harvest, plants were carefully removed and were then separated into roots, leaves, stems and pods. Sub-samples of pods and seeds were taken from each treatment to estimate the shelling percentage and 1-seed weight. The respective weights of roots, leaves, stems, pods and seeds were recorded after oven-drying these components to a constant weight for 3 days at 8 C. Total dry weight and pod harvest index (the ratio of pod to total plant dry weight) were calculated from individual components. The data were then used to compute dry weight per hectare. Disease assessment Occurrence and severity of leafspot were assessed based on visual rating using the ICRISAT scale of 1 9, where 1 = no disease or defoliation and 9 = > 8% defoliation (Subramanyam et al., 1995). Defoliation was also assessed by counting the number of nodes with missing leaflets above fourth node and the total number of nodes on the main-stem (Pixley et al., 199). The first four nodes were not considered due to formation of the branches from these nodes. These assessments were done at 15-day intervals during plant sampling for growth analysis. Disease assessments at all times were done by one person therefore not influenced by rater (Nutter et al., 1993). Model inputs The daily weather data, i.e. maximum and minimum temperature, rainfall and sunshine hours were collected. Data on sunshine hours were converted to solar radiation (MJ m - ) using the WeatherMan program in DSSAT V3.5 (Pickering et al., 1994). The data were used to prepare the weather input files. Similarly, the data generated from the detailed soil analysis at the experimental site were used to create water-holding, drainage, and runoff traits for the soil input file. The data on site (latitude, longitude and elevation), weather parameters, soil type and properties, initial conditions and crop management practices (cultivar and type, sowing date, spacing, plant population, irrigation and fertiliser management, residue handling, tillage practices and harvest schedules) were given as input to the model. For all simulations water balance was turned on; therefore, effects of water stress on growth and yield are accounted for based on rainfall, soil water-holding traits and crop growth. There was no observed or simulated water stress in all seasons, with an exception of one treatment in 1999 (last sowing date for long duration cultivar) where slight water stress was predicted. The model also accounts for nutrient limitations with soil fertility factor. This factor was set based on correct predictions of growth prior to disease incidence and defoliation. Based on our previous research in West Africa (Naab et al., 4) and Benin a value of.86 was given to these soils when compared to that of.9 for soils in Florida, USA. Thus the fertility yield gap could be viewed as 6% and was the same in all simulations. Data analyses For each year the data on the observed pod dry weight and total crop dry weight at final harvest were analysed using ANOVA using GENSTAT statistical package (Lawes Agricultural Trust, 1993). The accuracy of the model predictions with various disease assessment inputs was estimated using a statistical program (CROPSTA3) in DSSAT. The predicted values were compared to observed pod yield and total crop dry weight data across two cultivars and three sowing dates. The slopes and intercepts of linear regressions of simulated vs observed pod yield or total crop dry weight were computed. The following criteria were used to

4 47 M ADOMOU ET AL. assess model predictions for the best fit: (i) a lower root mean square error (RMSE); (ii) improved a (coefficient) and b (slope) values of linear regression between predicted and observed data (a close to zero, b close to 1. were indicators of improved model predictions); (iii) higher coefficient of determination (r ; close to 1.) and (iv) a higher index of agreement (d-value close to 1.; Willmott, 198). The RMSE reflects the magnitude of the mean difference between predicted and observed values over time and is calculated from the following equation: The coefficient of determination (r ) measures the degree of co-linearity between the observed and predicted values; it has a range of. to 1., with higher value indicating higher degree of co-linearity. It is estimated by the following equation: Index of agreement d-value is calculated from the following equation: The values of d range from to 1., with a value of 1 indicating perfect fit between the observed and predicted data. Although d value may be interpreted similarly to r, d value is considered superior to r and RMSE because it is less sensitive to outliers and unlike r, d value is also sensitive to additive and proportional differences. In all three equations, n is the number of observations, Oi is the observed variable at time i, Pi is the predicted variable at time i, O is the mean value of the observed variable over n observations, and P is the mean value of the predicted variable over n observations. Results Phenology and reproductive timing The data from the 1998 experiment were used to calibrate the CROPGRO-peanut (DSSAT V3.5) model for phenology and reproductive timing. The genotypic (cultivar) coefficients used in the model Table. Defi nitions and calibrated values of the genotypic coeffi cients for cvs TS 3-1 and used in experiment and simulation studies during 1998 and 1999 Genetic coefficient Cultivar TS Photothermal days* from emergence to flower appearance Photothermal days from first flower to first pod Photothermal days from first flower to first seed Photothermal days from first seed to physiological maturity Photothermal days from first flower to end of leaf expansion Maximum leaf photosynthesis rate (mg CO m - s -1 ) Specific leaf area (cm g -1 ) 7 6 Maximum size of full leaf (cm ) Maximum fraction of daily growth partitioned to seed and shell Maximum weight per seed (g) Photothermal days for seed filling for pod cohort Average seed number per pod (number pod -1 ) Photothermal days to reach full pod load *Photothermal days: days required if at optimum temperature and day length (alternative to growing degree days).

5 Simulating yield reduction due to leafspot in peanut 473 were adjusted to match data on phenology (dates of anthesis and maturity), initial slope of pod dry matter accumulation, and pod harvest indices. The model was calibrated following the sequence of steps described by Boote (1999). First, cultivar coefficients, i.e. photothermal days to first flower (EM-FL) and from beginning seed to maturity (SD-PM) were adjusted to match the observed phenology (R1 and R8 stages; Boote, 198) occurrence dates. TS 3-1 is an early maturing (9 day) cultivar and flowered in about 6 days, whereas was late maturing (1 day) and flowered in 39 days. Photothermal days (days required at optimum temperature and daylength) for emergence to first flower in TS 3-1 was 17.7 while for it was 9 (Table ). Secondly, the onset of pod formation (FL-SH), the duration of pod addition (PODUR) and the maximum partitioning coefficient (XFRUIT) were refined to match pod initiation and pod weight accumulation data observed under field condition. Cultivar TS 3-1 started to add pods earlier and its seed filling period was 4 days compared to 53 days for the late maturing cv (Table ). The final genotypic coefficients for the two cultivars used in the model input file are given in Table. Although there was no fungicide treatment in this study, comparing the results to previous studies in Benin showed that the model-predicted yields under rain-fed conditions were close to those observed in fungicide treatments of cultivars of similar durations. Similarly, the model predictions of potential yield under rain-fed conditions were similar to those in Ghana for short season duration cv. Chinese and long duration cv. F-mix under fungicide-sprayed treatments (Naab et al., 4, 5). Thus, once the inputs on soil conditions, cultivar traits and the crop phenology are accurate, the potential yields predicted by CROPGRO-peanut model would be close to those observed under fungicide trials. Disease occurrence In both years 1998 and 1999, leafspot symptoms were observed starting at 45 to 6 DAS (Fig. 1). The X-intercept of linear regression during initial rapid linear phase of disease occurrence showed that the occurrence of leafspot was earlier in later sowing dates (D and D3) when compared to earlier sowing dates (D1) in both cultivars. However, at maturity, neither cultivar showed a difference in leafspot scores in response to sowing date. The percent defoliation on the main-stem caused by leafspot showed significantly more rapid loss of 9 7 (A) 1998 TS 3-1 ( B) 1999 TS ICRISAT leaf spot score D1 D D Days after 45 sowing Fig. 1. ICRISAT leaf spot scores versus days after sowing in cvs TS 3-1 and sown on (A) 8 May (D1, circles), June (D, triangles) and 16 July (D3, squares) during 1998; and (B) June (D1, circles), 1 July (D, triangles) and 3 August (D3, squares) during Each data point represents the mean of four replicates

6 474 M ADOMOU ET AL. 1 (A) 1998 TS 3-1 (B) 1999 TS Main stem defoliation (%) D1 D D Days after 45 sowing Fig.. Main stem defoliation (%) vs days after sowing in cultivars TS 3-1 and sown on (A) 8 May (D1, circles), June (D, triangles) and 16 July (D3, squares) during 1998; and (B) June (D1, circles), 1 July (D, triangles) and 3 August (D3, squares) during Each data point represents the mean of four replicates leaf area for later sowing dates (Fig. ). Similar observations on effect of sowing dates and cultivars on leafspot occurrence (Fig. 1) and defoliation (Fig. ) were made during both years, with an exception for cultivar for leaf spot scores during 1999, which were similar across sowing dates. Pod and crop dry weight There were significant effects of sowing dates on pod yields and total crop weights during 1998 and 1999 (Table 3). Overall, across all the sowing dates the long duration cv produced significantly greater pod and total dry weight as compared to the short duration cv. TS 3-1 (Table 3). Later sowing dates gave significantly lower pod and total crop dry matter yields for both cultivars in 1999, but the effect was significant only for cv in 1998 (Table 3). Modeling yield using different disease assessment methods In the present research, percent defoliation was estimated from either (a) the observed main-stem defoliation; or (b) decline in the leaf mass over time from the measured peak values. Leafspot disease was rated using the ICRISAT scale (Subramanyam et al., 1995). Data on percent diseased leaf area (PDLA = percent necrosis) was developed from a linear regression based on the ICRISAT scale. This conversion was assisted by data of Bourgeois et al. (1991) and the leafspot lesion symptoms described in the ICRISAT leafspot scale (Subramanyam et al., 1995). Bourgeois et al. (1991) observed that percent visible disease started at zero at mid life cycle (at 4 to 6 days after sowing depending upon cultivar and sowing dates) and increased to a maximum of 9% at the end of the season. The ICRISAT disease scale describes the presence and absence of lesions in lower, middle and upper canopy at each rating; therefore disease score was weighted based on presence and severity of lesions in the respective canopy layers. The ICRISAT disease score of 1 is no disease and must have zero necrosis; whereas, disease score of is lesions present largely on lower leaves and must have a small but positive value, while disease score of 9 is almost all leaves defoliated. Thus, we scaled the percent necrosis linearly from zero PDLA at disease score of 1 to a maximum PDLA of 9% at disease score of 8 at which lesions are severe even on upper leaves. This gave a simple linear (y = a + bx) regression of y = 1.7x-1.17, where y is percent necrosis, x is visual disease score and a is intercept and b is slope. This equation was used to convert ICRISAT score into

7 Simulating yield reduction due to leafspot in peanut 475 Table 3. Observed pod dry weight and total crop dry weight at harvest for peanut cvs TS 3-1 and when sown on different dates under rain-fed conditions during 1998 and 1999 in northern Benin Sowing date Pod dry weight Total crop dry weight TS TS kg ha May June 16 July 117b 15a 1176b 647a 313b 199c SED 177 ** 9 NS 1999 June 1 July 3 August 1343a 1389a 756b 777a 38b 1385c 145b 639a 1393c 5156a 444b 314c SED 164 ** 37 *** **, *** Significant at P.5 and.1 probability levels, respectively; NS, not significant. Means within each cultivar and year followed by same letter are not significantly different (P.5). SED = Standard Error of Difference of means. PDLA. Model simulations were conducted using several of these approaches to determine the best method of disease assessment for determining effects of leafspot on dry matter production and yield. The values of percent defoliation estimated from each of the methods and the PDLA were entered in the model and used to decrease leaf area or photosynthesis (Batchelor et al., 1993). We used a virtual lesion ratio of 4. (beta or β value) in the model, as estimated by Bourgeois & Boote (199) for leafspot necrosis effect on peanut leaf photosynthesis, meaning there is an effective four-unit decrease in green photosynthesizing leaf area for every one unit of necrotic diseased area (Bastiaans, 1991). The No disease or def olia tion Leaf dry weight (kg ha -1 ) O b s e r v e d S i m u l a t e d disease only ( PDLA) Leaf mass loss +PDLA Mainstem defoliation Mainstem defoliation +PDLA D a y o f t h e y e a r Fig. 3. Observed (symbols) and simulated (lines) leaf dry weights over time of cv sown on 16 July during 1998 using different approaches for inputting disease effects. Each data point represents the mean of four replicates

8 476 M ADOMOU ET AL. model was then run to predict the subsequent effect on leaf, pod and total crop dry matter accumulation. For example, results of simulated leaf dry weight were compared to observed values over time for cv for the third sowing date (D3) during 1998 (Fig. 3). The model was able to accurately predict the leaf dry weight up to day of year 59 (i.e. 6 DAS), but thereafter the simulations with no disease inputs over-predicted the leaf weight. Under actual experimental conditions in the field, the observed leaf dry weights were much lower as there was a severe occurrence of leafspot and no control or preventive measures were taken. Similarly, the observed total crop dry weight was lower than predicted (data not shown) because of loss of leaf weight and subsequent loss of photosynthetic capacity. Various disease assessment methods were tested and shown in Fig. 3. The simulations with only necrosis effect (% PDLA) over-predicted leaf dry weight, as the scores were based on the occurrence of disease on the remaining leaves and did not take into consideration the leaves that were already defoliated due to occurrence of leafspot. The simulation with input of PDLA plus defoliation estimated from decline in leaf mass loss from its one-time observed peak values also over-estimated the leaf dry weights. On the other hand, the simulations with PDLA plus main-stem defoliation (based on the number of missing leaflets on the main-stem above fourth node) were close to the observed values and gave the best fit (Fig. 3). When simulations were run with main-stem defoliation but without PDLA, the model over-predicted the leaf dry weight, which suggested that the PDLA function was essential to accurately predict disease effects. Nevertheless, the defoliation effect was more dominant and more important as shown by Bourgeois et al. (1991). Statistical analysis of best method of disease assessment to predict yield To further test the methods of disease assessment input to the model, simulations were conducted using the different defoliation methods for both cultivars across all three sowing dates. The simulations and model fits were tested by comparing the root mean square errors (RMSE) and d-values for total crop dry weight (Table 4) and pod yield (Table 5) for six disease assessment methods separately, and average pod yields and biomass over cultivars and sowing dates were shown. The results showed that across all sowing dates and cultivars in both years the model simulations with no defoliation and no disease function over-predicted total crop dry weight and pod yields. Model runs with no defoliation but only PDLA, or with defoliation but no PDLA function over-predicted biomass and yield confirming that both factors were important components for assessing crop losses due to leafspot. Accounting for defoliation based on leaf mass loss (from measured one-time peak leaf mass data) appeared inadequate, presumably because the method misses the true peak leaf mass and thus underestimates percent defoliation. Considering both years, the model runs with the inputs of both main-stem defoliation and PDLA Table 4. Statistics of crop total dry weight as simulated by CROPGRO-peanut model with different disease input conditions during 1998 and 1999 in northern Benin (n=6 for each year) Run Disease input condition Observed mean kg ha -1 Predicted mean a b RMSE d r No defoliation and no disease Disease only (PDLA) Leaf mass loss Leaf mass loss + PDLA Main stem defoliation Main stem defoliation + PDLA No defoliation and no disease Disease only Leaf mass loss Leaf mass loss + PDLA Main stem defoliation Main stem defoliation + PDLA RMSE=root mean square of error; a and b are the values of linear regression of predicted vs observed data and d is the index of agreement (Willmott, 198).

9 Simulating yield reduction due to leafspot in peanut 477 Table 5. Statistics of pod dry weight as simulated by CROPGRO-peanut model with different disease input conditions during 1998 and 1999 in northern Benin (n=6 for each year) Run Disease input condition Observed mean kg ha -1 Predicted mean a b RMSE d r No defoliation and no disease Disease only (PDLA) Leaf mass loss Leaf mass loss + PDLA Main stem defoliation Main stem defoliation + PDLA No defoliation and no disease Disease only Leaf mass loss Leaf mass loss + PDLA Main stem defoliation Main stem defoliation + PDLA RMSE = root mean square of error; a and b are the values of linear regression of predicted vs observed data and d is the index of agreement (Willmott, 198). Pod dry weight (kg ha -1 ) (A) 1998 D1 [Observed(O)] D (O) D3 (O) TS 3-1 D1 Simulated without disease (S) D1 + Simulated with disease and defoliation function (DS) D (S) D + DS D3 (S) D3 + DS (B) 1999 TS Day of the year Fig. 4. Observed (symbols) and simulated (lines) pod dry weight over time (day of the year) with (broken lines) and without (solid lines) disease and defoliation inputs of cultivars TS 3-1 and sown on (A) 8 May (D1, circles), June (D, triangles) and 16 July (D3, squares) during 1998; and (B) June (D1, circles), 1 July (D, triangles) and 3 August (D3, squares) during Each data point represents the mean of four replicates.

10 478 M ADOMOU ET AL. generally had the lowest RMSE, greater d-values, intercepts (a) closer to zero, and slopes (b) closer to 1., for final total dry weight and pod yield (Tables 4 and 5). We thus conclude that this is the best method for inputting and predicting effects of leafspot disease on growth and yield. In addition, time series predictions of pod mass and total crop biomass were much closer to the observed values. Fig. 4 shows satisfactory predictions of pod mass over time by this disease assessment method. Therefore, these input functions can be used to simulate the effects on pod yield and total dry matter and to estimate the yield gap due to occurrence of leafspot disease. Since high r of regression were observed with high RMSE, but biased slopes and intercepts (Run 1 in Tables 4 and 5 for example) it is generally concluded that r is of little value in model parameter evaluation as indicated in the section on data analyses. Simulated disease effects on pod yield and total dry matter Results of model simulations of pod mass over time for TS 3-1 and for three sowing dates during 1998 and 1999 with and without disease effects are presented in Fig. 4. Without disease effects, the model was able to predict pod dry matter accumulation accurately only for the first to 3 days of pod growth after which disease reductions on leaf area and assimilation become important. Thereafter, the model without the defoliation and disease function over-predicted pod growth and yield; because the model assumes that there is no disease and predicts potential yields. Once the disease subroutine is turned on and the disease and the main stem defoliation functions are used, then the model predictions of pod growth (Fig. 4) and final yield (Table 5) and total crop dry weight (Table 4) were significantly improved and close to the observed values. Discussion Occurrence of late leafspot disease was severe in both early and late maturing cultivars in 1998 and The occurrence and progression of leafspot was earlier in later sowing dates. Furthermore, percent defoliation on the main-stem caused by leafspot was also more rapid for later sowing dates resulting in greater yield losses when compared to early sowing dates. This may be because the latersown crop is exposed to more humid conditions early in its life cycle plus presence of inoculum from adjacent fields, all congenial for greater occurrence of leafspot. Other research has shown that the reduction in pod yield due to occurrence of leafspot is mainly attributed to leaf loss and reduced leaf area index due to defoliation (Boote et al., 198; Subramanyam et al., 199; Nutter & Littrell, 1996). There is a minor additional component of yield loss that is attributed to the effect of necrotic spots on photosynthesis (Bourgeois & Boote, 199). The CROPGRO-peanut model allows the entry of crop damage (scouting information on percent defoliation and percent necrosis caused by disease) which decreases leaf area index and photosynthesis, causing a reduction in dry matter production and pod yield (Batchelor et al., 1993; Boote et al., 1993). Our research showed that CROPGRO-peanut model was able to simulate the observed pod and total crop dry matter over time when input on percent diseased (necrotic) leaf area and percent defoliation were provided. However the effects varied with method of estimation of the crop damage inputs. The best fit was obtained with inputs on observed percent main-stem defoliation above the fourth node and percent leaf necrotic area from disease (Fig. 3). These results showed the importance of proper methods to estimate diseased leaf area and defoliation caused by leafspot in peanut. Our study has shown that the long duration cultivar produced greater yield than the short duration cultivar TS 3-1. However the severity of disease as measured by disease scores and defoliation were similar in both cultivars when at similar days after sowing (Figs 1 and ). This suggests that both cultivars were equally susceptible to disease, but higher yield at maturity in long duration cultivar was due mainly to longer vegetative period and greater leaf area which may have allowed it to better withstand disease-induced defoliation and necrosis by leaf replacement. Research in Benin and West Africa has identified leafspot as a major foliar disease in this region (ICRISAT, 1991; Ntare & Waliyar, 1994; Waliyar, 1991). Studies have shown that application of fungicide sprays can be successfully used to control leafspot disease and improve crop yields up to 5% in Western and Southern Africa (ICRISAT, 1991; Kannaiyan & Haciwa, 199; Waliyar et al., ; Naab et al., 5). Trials at the research station in Ina (Benin) showed that application of fungicide to control foliar disease increased pod yield by 5% to 84% compared to untreated controls (ICRISAT, 1991). Currently, research is underway in Benin to identify suitable fungicide application schedules for the cultivars used in our study. We conclude that early sowing and long duration cultivars result in relatively better yields and that the mechanistic CROPGRO-peanut model can be used to simulate the influence of foliar diseases (leafspot) on growth and dry matter production for both short and long duration cultivars under different sowing dates when inputs on percent main-stem and diseased (necrotic) leaf area were provided. The best method of disease assessment was to input measured percent main-stem defoliation above the fourth node and

11 Simulating yield reduction due to leafspot in peanut 479 estimated percent diseased leaf area from visual disease scores. This approach may be extended to other important peanut foliar disease of the region such as rust and also for similar types of diseases in other crops. Acknowledgements We acknowledge financial support from the United States Agency for International Development, Peanut Collaborative Research Support Project. This manuscript is approved for publication as Florida Agricultural Experiment Station Journal Series No. R-856. References Adomou M Groundnut cultivation in the People s Republic of Benin. In Summary Proceedings of the first ICRISAT Regional Groundnut Meeting for West Africa, September 1988, Niamey, Niger, pp Patancheru, Andhra Pradesh, India: ICRISAT. Bastiaans L The ratio between virtual and visual lesion size as a measure to describe reduction in leaf photosynthesis of rice due to leaf blast. Phytopathology 81: Batchelor W D, Jones J W, Boote K J, Pinnschmidt H O Extending the use of crop models to study pest damage. Transactions of the American Society of Agricultural Engineering 36: Boote K J Growth stages of peanut (Arachis hypogaea L.). Peanut Science 9:35 4. Boote K J Concepts of calibrating crop growth models. In DSSAT, Version 3. A Decision Support System for Agrotechnology Transfer. Vol. 4, pp Eds G Hoogenboom, P W Wilkens and G Y Tsuji. Honolulu, HI: University of Hawaii. Boote K J, Jones J W, Smerage G H, Barfield C S, Berger R D Photosynthesis of peanut canopies as affected by leafspot and artificial defoliation. Agronomy Journal 7:47 5. Boote K J, Jones J W, Mishoe J W, Berger R D Coupling pests to crop growth simulators to predict yield reduction. Phytopathology 73: Boote K J, Batchelor W D, Jones J W, Pinnschmidt H, Bourgeois G Pest damage relations at the field level. In Systems Approaches for Agricultural Development, pp Eds F W T Penning de Vries and P S Metselaar. Dordrecht, The Netherlands: Kluwer Academic Publishers. Boote K J, Jones J W, Hoogenboom G, Pickering N B The CROPGRO model for grain legumes. In Understanding Options for Agricultural Production, pp Eds G Y Tsuji, G Hoogenboom and P K Thornton. Dordrecht, The Netherlands: Kluwer Academic Publishers. Bourgeois G, Boote K J Leaflet and canopy photosynthesis of peanut affected by late leaf spot. Agronomy Journal 84: Bourgeois G, Boote K J, Berger R D Growth, development, yields, and seed quality of Florunner peanut affected by late leaf spot. Peanut Science 18: Chiteka Z A, Gorbet D W, Shokes F M, Kucharek T A Components of resistance to early leaf spot in peanut genetic variability and heritability. Soil and Crop Science Society of Florida Proceedings 56: FAO. 3. Food and Agricultural Organization Production Year Book. Rome, Italy: FAO. ICRISAT ICRISAT West Africa Program Annual Report Sahelian Center, Niamey, Niger: ICRISAT. Kannaiyan J, Haciwa H C Economic benefits of spraying fungicides to control groundnut foliar disease in Zambia. Tropical Pest Management (United Kingdom) 36:1. Lawes Agricultural Trust GENSTAT 5 Reference Manual. Oxford, UK: Clarenden Press. Naab J B, Singh P, Boote K J, Jones J W, Marfo K O. 4. Using CROPGRO-peanut model to quantify yield gaps of peanut in the Guinean Savanna Zone of Ghana. Agronomy Journal 96: Naab J B, Tsigbey F K, Prasad P V V, Boote K J, Bailey J E, Brandenburg R L. 5. Effect of sowing date and fungicide application on yield of early and late maturing peanut cultivars grown under rainfed conditions in Ghana. Crop Protection 4: Ntare B R, Waliyar F The role of genetic enhancement in sustainable groundnut production in West Africa. In Sustainable groundnut production in Southern and Eastern Africa: Proceedings of workshop, 5 7 July 1994, Mbabne, Swaziland, pp Patancheru, India: ICRISAT. Nutter F W Jr, Littrell R H Relationship between defoliation, canopy reflectance and pod yield in peanut-late leaf spot pathosystem. Crop Protection 15: Nutter F W Jr, Teng P S, Shokes F M Disease assessment terms and concepts. Plant Disease 75: Nutter F W Jr, Gleason M L, Jenco J H, Christians N C Assessing the accuracy, intra-rater repeatability, and interrater reliability of disease assessment system. Phytopathology 83: Pickering N B, Hanson J W, Jones J W, Wells C M, Chan V K, Godwin D C WeatherMan: a utility for managing and generating daily weather data. Agronomy Journal 86: Pixley K V, Boote K J, Shokes F M, Gorbet D W Disease progression and leaf area dynamics of four peanut genotypes differing in resistance to late leafspot. Crop Science 3: Subramanyam P, Wongkaew S, Reddy D V R, Demski J W, McDonald D, Sharma S B, Smith D H Field Diagnosis of Groundnut Diseases. Information Bulletin No. 36. Patancheru, Andhra Pradesh, India: ICRISAT. Subramanyam P, McDonald D, Waliyar F, Reddy L J, Nigam S N, Gibbons R W, Ramanatha Rao V, Singh A K, Pande S, Reddy P M, Subba Rao P V Screening Methods and Sources of Resistance to Rust and Late Leaf Spot of Groundnut. Information Bulletin No. 47. Patancheru, Andhra Pradesh, India: ICRISAT. Thornton P Applications of Crop Simulation Models in Agricultural Research and Development in the Tropics. Muscle Shoals, Alabama, USA: International Fertiliser Development Center. Waliyar F Evaluation of yield losses due to groundnut leaf diseases in West Africa. In Summary Proceedings of the second ICRISAT Regional Groundnut Meeting for West Africa, September 199, Niamey, Niger, pp Patancheru, Andhra Pradesh, India: ICRISAT. Waliyar F, Adomou M, Traore A.. Rational use of fungicide applications to maximise peanut yield under foliar disease pressure in West Africa. Plant Disease 84: Willmott C J Some comments on the evaluation of model performance. Bulletin of the American Meteorological Society 63: (Revised version accepted 1 March 5; Received 7 September 4)

12