Anand Agricultural University, Anand

Anand Agricultural University (AAU) was established in 2004 at Anand with the support of the Government of Gujarat, Act No.(Guj 5 of 2004) dated April 29, 2004. Caved out of the erstwhile Gujarat Agricultural University (GAU), the dream institution of Sardar Vallabhbhai Patel and Dr. K. M. Munshi, the AAU was set up to provide support to the farming community in three facets namely education, research and extension activities in Agriculture, Horticulture Engineering, product Processing and Home Science. At present there seven Colleges, seventeen Research Centers and six Extension Education Institute working in nine districts of Gujarat namely Ahmedabad, Anand, Dahod, Kheda, Panchmahal, Vadodara, Mahisagar, Botad and Chhotaudepur AAU's activities have expanded to span newer commodity sectors such as soil health card, bio-diesel, medicinal plants apart from the mandatory ones like rice, maize, tobacco, vegetable crops, fruit crops, forage crops, animal breeding, nutrition and dairy products etc. the core of AAU's operating philosophy however, continues to create the partnership between the rural people and committed academic as the basic for sustainable rural development. In pursuing its various programmes AAU's overall mission is to promote sustainable growth and economic independence in rural society. AAU aims to do this through education, research and extension education. Thus, AAU works towards the empowerment of the farmers.

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Open Access
STUDIES ON SOME ASPECTS OF PHYTO ETEOROLOGY OF WHEAT (Triticum aestivum L.) AT ANAND
(AAU, Anand, 1985) SAVANI, M. B.; MISTRY, P. D.
The changeability of meterological factors in time and space is diverse. The most important weather variables which have to be considered as limiting plant growth and development are temperature, moisture and light (or solar radiation). The distribution of these meteorological parameters during the growing season of the crop shows great variation in the different locations of the same region and consequently, a great variability in the crop production.
Open Access
STUDIES ON THE ASSESSMENT OF THE PERFORMANCE OF SOME CROP-WEATHER MODELS UNDER ANAND CONDITIONS
(AAU, Anand, 1985) SHEKH, A. M.; MISTRY, P. D.
Crop yield forecasting is quite important to monitor the economy of any country which is predominantly agricultural. Crop-weather Hodles are formulations which mathematically relate the crop yield with the agrometeorological parameters like evapotranspiration or crop transpiration (Tc). Very little information is available on the performance of available crop-weather models under the Indian conditions. With this background, a study was planned and conducted to test and validate the performance of different crop-weather models, during the rabi season of the years 1981-82, 1982-83 and 1983-84 with Sonalika (HDM-1553) variety of wheat. The evapotranspiration (ET) losses were measured by gravimetric soil moisture measurements which were termed actual evapotranspiration (AET) and were also estimated by Priestley-Taylor (1972) approach for obtaining potential evapotranspiration (PET).
Open Access
ENERGY BALANCE OVER A WHEAT FIELD WITH SPECIAL REFERENCE TO EVAPOTRANSPIRATION AND PHOTOSYNTHESIS
(AAU, Anand, 1980) Mehta, Arvind N; MISTRY, P. D.
Evapotranspiration and photosynthesis are among the most vital plant responses to energy that govern the productivity of crops in the field. The energy required for both the processes being derived from the net available energy over the field, a study of energy balance over a crop field is important in the understanding of the crop responses to weather. A field study to measure the energy balance components over the field of wheat, variety Sonalika (H.D.M. 1553) was carried out to estimate the energy utilization in evapotranspiration and photosynthesis and also to study other pertinent aspects of water use by crops.
Open Access
ESTIMATING WHEAT YIELDS IN GUJARAT USING WTGROWS AND INFOCROP MODELS
(AAU, Anand, 2003) AKULA, BABY; Shekh, A. M.
Crop simulation models are valuable tools to researchers to help them to understand the influence of climatic variables on crop productivity. The model estimated yields are handy to the agencies in government, trade and industry for planning about distribution, storage, processing, export or import of crop produce. Yield estimates by the models are also useful in taking timely policy decisions on fixing levy prices, because the estimates of the yield are available well in advance of the actual harvesting of the crop. Hence, a two-pronged approach was followed to estimate wheat yields in Gujarat, with the help of WTGROWS and InfoCrop simulation models. Initially both the models were calibrated and validated under Anand conditions through field experiment laid out in a strip plot design with three replications during rabi season of the years 2000 and 2001. Tliree dates of sowing were assigned as a main plot treatment with four irrigation regimes as sub plot treatments. Consistently higher yields were realised in case of the second date of sowing (15* Nov) during both the years although the yield differences were not statistically significant. Relatively more yields were realised in 2000 than those realised in 2001 and this was due to prevalence of favourable low temperatures during 50-90 DAS - a period that corresponded with anthesis to dough stage in conjunction with intermittent cold spells from 70-75 DAS corresponding with soft dough phase in the former year. In contrast to what was observed in case of yields in relation to the dates of sowing, yield data due to different irrigation treatments showed significant differences among them. Three irrigations gave significantly the lowest yield as compared with yields realised through any other irrigation treatment. The lowest yields realised in the treatment involving three irrigations were due to prevalence of moisture stress during tillering and flowering. Paradoxically, six irrigations despite not missing any important physiological stage, did not record significantly higher yield in comparison with yield in response to five irrigations. This was on account of the fact that, luxurious vegetative growth in the former case had caused lodging, as the prevailing wind speed was high. Different test criteria were followed to validate the performance of the models. Besides, error per cent was also calculated in all the different treatments to express the deviation in simulated values from those observed. Close scatter of simulated yield and total dry matter and respective measured values around the regression line and 1:1 line in case of both the models indicated good agreement between them. Both the models exhibited their robustness in predicting yields by explaining more than 90 per cent of variation in yield and total dry matter on an overall basis. However, there still remains some scope for improvement of the models in accounting for the loss due to lodging. The estimated RMSE for yield by WTGROWS was 318 kg ha-1, while that for yield by InfoCrop was 360 kg ha-1. Among the different dates of sowing, error per cent was relatively low in the treatments of the second date of sowing when compared with that for other dates. Both the models displayed decrease in error per cent with increase in irrigation levels. Underestimation of the simulated yield was more when the number of irrigations was less [three (I1) and four (I2)] when compared with that for more irrigations [five (I3) and six (I4)]. The underestimation was relatively more in case of InfoCrop, than that in case of WTGROWS. The performance of the models could be adjudged with the index of agreement (D), which was relatively high for WTGROWS (D= 0.97) than that for InfoCrop (D=0.95) in terms of yield. The models were also observed to perform in a similar way in terms of their response to the treatments in case of total dry matter, phenology and LAI also. The days to anthesis and maturity were simulated with less accuracy by both the models as compared to that of yield. Anthesis by WTGROWS explained more variance (R2=0.82) than that explained by InfoCrop (R2=0.75). The performance of these models in explaining the variance due to days to maturity was reverse of what was observed in case of anthesis. The highest and the .lowest ET were observed in case of the treatments of D2I4 and Dili, respectively. WTGROWS also showed similar pattern. Relatively higher proportion of MBE as compared to that of MAE during both the years in terms of ET as simulated by WTGROWS revealed under- prediction of ET by the model. Nonetheless, the error per cent did not cross the limit of -15 per cent during both the seasons except in case of Dill (-15.77%). Both the models expressed sensitivity to weather parameters viz., temperature, radiation and CO2 levels under both potential and stressed test conditions. But, the magnitude of change from the respective base yields in case of both the models was more to temperature under stressed conditions. However, the magnitude of response was more in case of WTGROWS than that in case of InfoCrop on overall basis except in case of radiation under stressed conditions where InfoCrop exhibited relatively more sensitivity. Linear response to TTVG, POTGWT, GNODMA, NSOILI, WLSTI was observed in case of both the models. The sensitivity was relatively more in case of WTGROWS than in case of InfoCrop. Moreover, InfoCrop exhibited linear response to RGRPOT and SLAVAR also. Statistical analysis of the historical actual wheat yield data of the state revealed that the average actual yield for the state as a whole was 2.5 t ha-1. Out of the ten districts selected to understand the temporal and spatial variability in wheat production levels and further to estimate yield gap through linking the model results with GIS, only Junagadh, Banaskantha and Bhavnagar exhibited significant positive linear trend at an average increase rate of 66, 31 and 25 kg ha-1 yr-1, respectively. Majority of the other districts failed to exhibit any discernible linear trend. However, Mehsana was found to be the second potential wheat producer of the state after Junagadh. The estimated average district potential yield by the models was 5.9 t ha-1 on overall bases. This is 2.36 times higher than the average actual state yield and is due to favourable thermal regimes as it was evident under Anand conditions where the estimated TTVG explained 87 per cent of variation in the potential yield and indicated significant linear positive trend. Similar reasoning holds good for higher potential yields in other districts. The attainable yields were estimated by imposing the management constraint of delayed sowing by twenty days from the optimum time (15thNov). The attainable wheat yields were found to decrease in all the districts irrespective of the agro climatic zone. The estimated attainable yield for the state as whole was 4.8 t ha-1 on the basis of the ten districts considered in the study. The average sowing yield gap between potential and attainable yield varied from 863 to 1205 kg ha-1 Reduction in yield due to delayed sowing was highest in the districts of Saurashtra which was followed in this respect by middle Gujarat, north Gujarat and south Saurashtra in sequence. The quantity of reduction in succession in these agro climatic zones was to the tune of 60, 59, 49 and 44 kg ha-1 per day delay in sowing, respectively.
Open Access
CROP-PEST-WEATHER INTERACTION AND POPULATION DYNAMICS OF (Heliothis armigera (Hubner) IN TWO DIVERSE PIGEONPEA (Cajanus cajan (L.) Millsp.) GENOTYPES (BDN-2 and GT-100) AT ANAND
(AAU, Anand, 1998) Chaudhari, G. B.; Shekh, A. M.
The results obtained in this investigation revealed that the air temperature and photoperiod had profound influence on growth and development of the pigeonpea crop. The variation in air temperatures during different phenophases resulted in differential attainment of physiological maturity in both the genotypes. Whereas, the differential availability of bright hours of sunshine (BSS) during reproductive phase resulted in higher seed yield. Low vapour pressure (VP) and relative humidity (RH) during flower bud initiation to podding phase, were favourable for higher seed yield. The seed yield and other yield attributing characters of pigeonpea crop were significantly influenced by the different treatments. The seed yield of protected condition was observed 36% higher than that under unprotected condition. The short duration genotype, GT-100 was found significantly higher in seedyield than the long duration genotype BbN-2. The seed yield was found to decrease upto 35%, with delayed sowing till 40 days after the onset of monsoon. Significant differences in total biomass were noticed in treatments like irrigation, genotypes and dates of sowing The results from correlation study revealed that there was a positive significant association between seed yield and different weather parameters like, maximum and minimum temperatures, bright hours of sunshine and different thermal indices like, growing degree days, phototherraal units and Heliothermal units and accumulated PAR. It has been observed that there was a difference in growing degree days requirement for the two genotypes to attain different phenological phases. To attain physiological maturity the GDD requirement for BDN-2 was 3105°Cd and it was 2894°Cd for GT-100 genotype
Open Access
DYNAMIC MODELING OF DAILY WATER USE BY SUMMER PEARL MILLET ' (Pennisetum americanum L.)
(AAU, Anand, 1995) Bodapati, Papuji Rao; Savani, M. B.
Crop water use is a complex function of the climatic conditions, stage of the crop development and the soil water content. Models have been developed earlier using various approaches and levels of details to improve the prediction of evapotranspiration. Functional models with some empiricism can be used for routine applications than the mechanistic models. Transpiration from the pearl millet was found to be strongly influenced by leaf area than by stomatal conductance. Field experiments during the summer season of the years 1994 and 1995 were conducted with pearl millet cv. GHB-30. The experiments were laid out in split-plot design, with three dates of sowing as the main plot and four irrigation levels as the sub-plot treatments which were replicated four times. The results obtained in this investigation revealed that, air temperature had a profound influence on the growth and development of summer pearl millet. The optimum date of sowing was found to be February 15th , which would provide optimum environmental conditions for the growth and development of the crop. Different dates of sowing did not show any significant effect on the grain yield. Irrigating the crop at 25% depletion of available soil moisture gave the highest grain and biomass yields but its WUE was lower than that for the other irrigation levels. Pearl millet required about 310 GDD in summer season to build considerable GLAI and about 800 GDD to attain the maximum GLAI. A second-order polynomial was developed for estimating GLAI using the accumulated GDD. The FAO Kc, values had over-estimated ET rates and a second-order polynomial was developed to estimate daily Kc values from the accumulated GDD for non-stressed pearl millet. The rate of ET in pearl millet was found to decrease with an increase in soil moisture deficit and approached zero at a soil moisture depletion of 65% of the available soil moisture. PLANTGRO and MCD models when evaluated against the field data collected through this experiment, predicted ET reasonably better for nonstressed treatments than for stressed treatments. Of the two models, the MCD model predicted better for stressed condition than the PLANTGRO model. The functional relations for the PET estimation and root water uptake in the MCD model needed substantial modification. The separation of the PET in the PLANTGRO model did not suit the summer pearl millet. A one-day time step model BAJRAWAT had been developed in the 'C' language during the course of the present study, and was made User-friendly. Irrigation amount and the PET being its main driving forces, the partitioning of PET into soil evaporation and transpiration had been accomplished in BAJRAWAT by GLAI. The actual evaporation and transpiration depended on the availability of water in the surface soil and in the root zone and also on the depth of root penetration. The evaporation was assumed to take place from the surface soil only and the soil was further divided into four layers, from which water was assumed to have been removed by transpiration and drainage. Infiltration was assumed to have been taking place depending on the amount and the location of water already in the soil layers. The transpiration was computed as a function of GLAI and the available moisture in the root zone. The development of GLAI was considered to be controlled by thermal time and a moisture stress factor. The BAJRAWAT model when validated along with PLANTGRO and MCD models predicted ET better than the latter two models. The relative transpiration of summer pearl millet was found to be more closely associated with relative dry matter yield than with the relative grain yield
Open Access
RESPONSE OF CAULIFLOWER (Brassica oleracea var. botrytis) TO WEATHER WITH VARYING IRRIGATION SCHEDULES AND TESTING OF VEGETABLE MODEL FOR MIDDLE GUJARAT AGROCLIMATIC ZONE
(AAU, Anand, 2005) B., AJITHKUMAR; SAVANI, M. B.
Cauliflower (Brassica oleraceae var. botrytis) is one of the most popular winter vegetable crops grown in India. The cultivation of the crop has been found to be highly remunerative under irrigated condition during rabi season and hence gaining popularity among the vegetable growing farmers of Gujarat state. The crop requires certain cardinal levels of various factors of environment like air and soil temperature, quality, intensity and duration of radiation, humidity of air and soil etc for its optimum physiological functioning. Since, the effects of weather on curd yield are complex, deeper and clear understanding of how the climatic factors affect the growth and yield of cauliflower. A field experiment during rabi seasons of the years 2002-03 and 2003-04 was therefore laid out in a split plot design with three replications. The three dates of planting were assigned as main plot treatments, three irrigation regimes as sub plot and two spacing as sub- sub plot treatments. The results obtained during the course of study revealed that the weather had played a significant role in deciding the yield of cauliflower. However, the weather variables had affected the crop growth and yield differently in different phenophases during its growing period. The results regarding the curd yield as well as the biomass of cauliflower as influenced by the different dates of planting showed that Di planted crop produced significantly higher curd yield as well as biomass. The favorable weather conditions in D1 planted crop influenced the entire physiology of the crop culminating into the higher yield. D3 planted crop which encountered unfavourable weather conditions like the high temperature and high solar radiation during the curd maturity phase resulted in leafy and small curds. Irrigation scheduled through IW / CPE = 1.O and spacing of 60 cm x 45 cm proved the best. The correlation and regression studies between the weather elements and the time taken for completion of the phenophases revealed that certain weather parameters significantly correlated with the time taken for completion of the corresponding phenophase. The number of days taken to juvenile phase was negatively and significantly correlated with the mean values of the T max, T min, Tmean, RH1, VP1, VP mean, BSS and EP. The maximum, minimum and mean temperatures were also negatively and significantly correlated with number of days during the curd induction phase and curd maturity phase. Regression models were developed for prediction of phenophases using different weather variables. The correlations were worked out between the days taken to attain a specific phenophase and agrometeorological indices encountered to complete the phenophase were found positive and significant. The regression model was developed for the prediction of the number of days required for completion of different phenophases on the basis of agrometeorological indices. GDD and PTU yielded the lowest coefficients of variation and as such they were considered as better heat unit indices for prediction of the cauliflower yield during the rabi season for the middles Gujarat agro climatic conditions. The results on germination studies revealed that the number of days taken to reach 80 per cent germination was lower in the first year than that in the second year. The mean air temperature ranged from 28.9 to 31.8°C and the soil temperature from 30.5°C to 32.0°C which had favoured the germination of the seeds. The LAI values revealed that the development of the leaf area remained slow during the early vegetative growth period (juvenile phase) upto 30-45 days after planting and thereafter, increased sharply with the advancement of the crop age during both the years in all the dates of planting. The heat use efficiency increased with advancement of the age of the crop. There was a gradual decrease in k value with the advancement of the crop to maturity. The rate of crop growth was low in the first 31-45 days after planting and thereafter increased with crop age.
Open Access
CROP WEATHER RELATIONSHIPS IN SUMMER PEARL MILLET (Pennisetum americanum (L.) Leeke) AND TESTING OF CERES MILLET MODEL FOR THE MIDDLE GUJARAT AGROCLIMATIC ZONE
(AAU, Anand, 1993) Maniyar, Vijayprakash Govindlalji; MEHTA, A. N.
Pearl millet fPennisetum americanum (L.) Leeke) is one of the most important and widely cultivated cereal crop in the arid and semi-arid regions of the world. It is generally, grown as rainfed crop in rainy season in the scanty rainfall area and on poor soils. It is more resistant to drought than sorghum. It can sustain under higher temperature regimes. Under intensive cropping system, it has its own place and being cultivated during summer season, where irrigation facilities are available. Weather variables affect the crop growth differently in different phenophases during its growth cycle. Field experiments during summer season of 1991 and 1992 were conducted with Cv.GHB-30 and were laidout in split plot design, with three dates of sowing as main plot and four irrigation regimes as sub-plot, replicated thrice. The results obtained during the course of investigation revealed that, higher temperatures decreased the duration of the crop with the delay in sowing. Higher relative humidity during flowering stage gave higher grain yields. Higher grain yields were recorded from first date of sowing. However, reduction was lesser in second date of sowing. Late sown crop recorded lowest grain yields. Irrigation scheduled through IW/CPE = 1.0 proved best. However, irrigation scheduled by infrared thermometry could save about 30 to 35% of irrigation water with no much reduction in grain yields. Yield attribute such as earhead weight had positively contributed towards grain yield. Consumptive use of water increased with increase in the frequency of irrigation. However, water use efficiency was higher with lesser irrigation frequencies, indicating a drought resistance trait in summer pearl millet. Stress degree days had significant negative correlation with growth attributes. Decrease in leaf water potential during flowering stage had adversely affected the grain yields. Studies on Intercepted photosynthetic active radiation indicated higher IPAR use efficiencies in D2 treatment (15th Feb.sowing date) during both the years. The extinction coefficient (K) calculated showed a value of 0.89. Correlation studies between grain yield and weather parameters revealed that morning, afternoon and mean relative humidity upto flowering stage played a major role in deciding the final grain yield. During anthesis to dough stage both thermal interception rate (TIR) and intercepted photosynthetic active radiation (IPAR) showed significant positive correlation with grain yield indicating better source sink relationship. Stepwise regression analysis selected a model with mean relative humidity (during emergence to tillering stage) and hours of bright sunshine (during flag leaf to anthesis stage) as parameters for predicting grain yields, 20 to 25 days before maturity. Path analysis of grain yield and weather parameters observed in important phenophases indicated that higher relative humidity during flag leaf to dough stage and higher thermal interception rate (TIR) during anthesis to dough stage are found favourable for higher grain yields. Prediction model obtained for total biomass production selected only accumulated growing degree days (GDD) during emergence to tillering phase (P1). This model could predict the total biomass 50 to 60 days before maturity. CERES millet model corrected for genetic coefficients was found to be good for this region. CERES millet model could predict the anthesis date and maturity date with minimum error. However, grain yields and total biomass production predicted by CERES millet model showed larger percent error compared to that of grain yields and total biomass production predicted by regression models obtained in the study. Secondly, CERES millet model has the limitation of predicting the yield at the end of growing season. However, the prediction models obtained in the study could predict the grain yields and total biomass production well in advance.
Open Access
SIMULATION MODELING OF WHEAT YIELD USING WOFOST MODEL UNDER MIDDLE GUJARAT AGRO CLIMATIC REGION
(AAU, Anand, 2013) MISHRA, SUDHIR KUMAR; Shekh, A. M.
Crop growth simulation models are useful tools for considering and exploring the complex interactions between a range of factors that affecting crop performance, including weather, soil properties and management. To sustain crop production and to reduce the detrimental effects of varied environmental conditions on crop production, the understanding of crop growth in relation to varying resource input and agro-environments is required for management options. Improved production technology at the farm level is the most crucial starting point for future improvement of productivity of wheat by employing and adapting suitable crop growth simulation models. In addition to this, the use of crop growth simulation models comes handy to the government agencies, trade and industry and for planning about the distribution, storage, processing, and export/import of crop produce besides taking timely policy decisions on fixing levy prices as they provide accurate advance estimation of yields. Crop simulation models are recent tools that have facilitated identification of production constraints and for assisting in agro-technology transfer. In the present investigation, WOFOST (WOrld FOod STudy) v.7.1 model was used to develop genetic coefficients and validate it under Anand conditions by conducting a field experiment on Sandy loam soil during two consecutive Rabi seasons of 2009- 2010 and 2010-2011. The study was carried out for simulation of phenology. growth and yield of four different wheat cultivars (cv. GW 322, GW 496, GW 366 and GW 1139), vahdation of four dates of sowing viz., D1 (1st November), D2 (15th November), D3 30th November and D4 (15th December), cahbration of genetic coefficients of all four different cultivars, carry out the sensitivity analysis of WOFOST model with respect to middle Gujarat agro-climatic region. The cultivar GW 322 performed best during 2009-2010, 2010-2011 and in pooled analysis in producing the grain yield by 3611 kg/ha, 4384 kg/ha and 3998 kg/ha. Among all cultivars, GW 322 performed best (3611 kg/ha in 2009-2010 and 4384 kg/ha in 2010-2011) followed by cultivar GW 496, GW 366 and GW 1139. Although the durum type wheat cultivar GW 1139 yielded least among all cultivars. The yield reduction is more under delayed sowing. Although this cultivar (GW 1139) is cultivated under rainfed conditions in Bhal zone but, under irrigated conditions in Anand its performance was better in terms of duration as well as in yield. The mean maximum temperature of wheat growing season during first year ranged between 30.4 to 31.4 °C while, during second year it was between 28.9 to 31.3 °C under different dates of sowing. Higher maximum temperature caused the reduction in wheat yield. With an increase of 1°C in maximum temperature the wheat yield reduced by 412 kg/ha. Among different stages, the flowering stage was most vulnerable with increase temperature. At this stage, every 1°C increase in the maximum, minimum and mean temperatures respective reduction in yield was 213 kg/ha, 177 kg/ha and 231 kg/ha. Minimum temperature less than 11.5 °C at flowering stage was found more favourable for wheat production. This lower minimum temperature corresponds with the wheat sown on the normal dates (15th November) hence, it is recommended. The association between simulated and observed grain yields of GW 496, GW 366 and GW 1139 cultivars were found satisfactory. Consistently higher grain yields were realized in case of the second date of sowing (15th November) during both the years and the yields were statistically significant for the pooled data over the years. Average minimum temperature in D2 sowing around 11.5°C around 70 DAS and 8.4°C at 60 DAS in 2009-2010 and 2010-2011, respectively seemed to have contributed to yields under D2 being higher than those under D1, D3 and D4. The average minimum temperature in D2 sowing was found the lowest and prevalence of lower mean temperature during flowering and milking stage during both the years were found favourable for higher grain yield. The WOFOST model was superior to InfoCrop and DSSAT crop simulation models in simulation of days to anthesis and maturity of wheat crop. Various test criteria were applied to validate the performance of the model. The simulation performance of grain yield was found better in 2009- 2010 than in 2010-2011. The calibrated WOFOST model performed well for simulating phenological stages (viz., anthesis and physiological maturity) with error percent less than 4.03%. Similarly, the error percent was less than 8.48 for simulation of grain and biomass production. Hence, this model can be used for simulating the phenology and yield of wheat cultivars. Highest error 9.34% was noticed in simulation of leaf area index by InfoCrop and 9.21% by DSSAT model. The comparison of WOFOST with DSSAT and InfoCrop models suggested the superiority of WOFOST over others as it was evident from least percent error in simulating phenology, yield and yield attributes of wheat. The DSSAT models also simulated the phenology, yield and yield attributes of wheat close to the observed. This model estimated the yield, biomass and harvest index with percent error less than 7.83, -5.5 and 11.47, respectively. Index of agreement from all three models was more than 0.95 in simulations of various growth and yield components of wheat cultivars under varied environmental conditions reveal the accuracy of models.

Anand Agricultural University, Anand

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