P. Vijaya Kumar

Influence of moisture, thermal and photoperiodic regimes on the productivity of castor beans (Ricinus communis L.)

Abstract Field experiments on rainfed castor beans ( Ricinus communis L.) were conducted for 4 consecutive years (1990–1993) to study the effect of weather on the yields of different spike orders of castor, viz., primaries, secondaries and tertiaries. In these experiments, castor Cv. Aruna was planted on three different dates (treatments) in each year. The results from this study revealed that higher bean yield can be achieved by planting the crop on an early date, i.e., in June. Early planting results in higher yield of primaries and lower yield of tertiaries compared to delayed planting and vice versa. Though primaries were main contributors to total yield (59%), their loss due to either biotic or abiotic stress was compensated by later developed spikes (secondaries and tertiaries). Multiple regression equations were worked out between yields from different spike orders and specific weather parameters like degree days, photo-periods and moisture adequacy index during their reproductive periods. The moisture adequacy index is the ratio of the actual to potential evapotranspiration. These studies revealed that primaries are mostly influenced by photo-period (61%) and to a lesser extent by moisture adequacy index (39%). Secondaries were mainly influenced by moisture adequacy index (83%) whereas tertiaries are not found to be influenced independently by any weather parameter but were inversely influenced by the interaction of moisture adequacy index and degree days (93%). Total yield, i.e., sum of the yield of primaries, secondaries and tertiaries was positively influenced by the moisture adequacy index and degree days during the total reproductive period (initiation of primaries to maturity of tertiaries). The model predicting total yield can be utilised to identify optimum planting period based on the climatic data of the region. The results from this study will be of further use in formulating suitable crop growth models to simulate yields of castor and also in delineating areas with optimum agroclimatic environments for achieving potential productivity of castor.

Radiation and water use efficiencies of rainfed castor beans (Ricinus communis L.) in relation to different weather parameters

Abstract Field experiments on rainfed castor beans ( Ricinus communis L.) were conducted for 4 years (1990–1993) to explain the variability in radiation use efficiency (RUE) and water use efficiency (WUE) in relation to meteorological parameters. In these experiments castor c.v. Aruna was sown on three different dates (treatments) in each year. The results from this study indicate that both RUE and WUE vary from year to year and also were influenced by the planting dates. Variations in RUE and WUE ranged from 0.79–1.19 g MJ −1 and 0.72–1.25 g kg −1 , respectively. The variability in RUE and WUE before and after flower initiation were quite contrasting. The variability in RUE and WUE was associated with saturation vapour pressure deficit (SVPD), drought index, temperature and wind velocity. RUE was related positively with SVPD and wind velocity and negatively with drought index and temperature. WUE showed inverse relationships with SVPD and temperature and a direct relation with wind velocity. The study emphasises the need for incorporation of the effect of weather on RUE and WUE in the algorithms for biomass estimation of crop simulation models.

Use of remote sensing for drought stress monitoring, yield prediction and varietal evaluation in castor beans ( Ricinus communis L.)

The current study was taken up to investigate the utility of remote sensing tools like infrared thermometer and spectral radiometer for screening of germplasm, stress monitoring and yield prediction in castor beans (Ricinus communis L.). The study was carried out through field experiments conducted for six years (1994–1999) at Hayatnagar Research Farm, Hyderabad, India. In each year, four cultivars of castor beans, viz. VP‐1, 48‐1, GCH‐4 and Aruna, were planted on two different dates maintaining an interval of 6–8 weeks so as to expose the crop to different environments. The infrared thermometric observations like canopy–air temperature differential (T c–T a) explained 50–60% variation in soil moisture status and showed a significant relationship with soil moisture. Yield of castor beans exhibited significant inverse relationship with T c–T a, which explained 59% of variation in yield. The hybrid GCH‐4, registering comparatively lesser mean T c–T a over the entire growing period, established itself as a better cultivar. The spectrometer observations also proved GCH‐4 to be a superior genotype in view of its higher reflectance in near‐infrared region of the spectrum. The significant negative relationship of T c–T a of GCH‐4 with saturation vapour pressure deficit brought out its drought tolerance trait over the other genotypes studied. These findings at field level can be extended to wider spatial level using satellite imageries.

Assessment of plant-extractable soil water in castor beans (Ricinus communis L.) using infrared thermometry

Abstract Assessment of plant-extractable soil water from experimental plots using infrared thermometer was carried out through a field experiment on rainfed castor beans (Ricinus communis L.) conducted for 2 years (1992–1993) at Hyderabad, India. The castor beans (cultivar: Aruna) were planted on three different dates in both years. Attempts were made to normalize canopy temperature and stress degree days (SDD) for environmental variability to accurately assess the plant-extractable soil water (PESW) using an Infrared thermometer. Normalization of SDD for variability of temperature and saturation deficit (division of SDD by air temperature and saturation deficit), greatly improved the predictability of the soil water status (PESW), than that based on SDD. The coefficients of determination (R2 values) of the relationship between PESW and SDD after normalization were 0.65 and 0.61 in the years 1992 and 1993, compared to 0.19 and 0.08 before normalization, in respective years. This simple method of normalization of SDD (the division of SDD with weather parameters), which seems to be a promising technique for assessing the soil water status through remote sensing techniques in semi-arid tropics (SAT) needs to be further tested in other environmental conditions and also in other crops, for realising the long-felt objective of assessing soil moisture status with the help of infrared thermometry.

Predicting Irrigated and Rainfed Rice Yield Under Projected Climate Change Scenarios in the Eastern Region of India

Numerous estimates for the coming decades project changes in precipitation resulting in more frequent droughts and floods, rise in atmospheric CO2 and temperature, extensive runoff leading to leaching of soil nutrients, and decrease in freshwater availability. Among these changes, elevated CO2 can affect crop yields in many ways. It is imperative to understand the consequences of elevated CO2 on the productivity of important agricultural crop species in order to devise adaptation and mitigation strategies to combat impending climate change. In this study, we have modeled rice phenology, growth phase, and yield with the “Decision Support System for Agrotechnology Transfer (DSSAT) CERES rice model” and arrived at predicted values of yield under different CO2 concentrations at four different locations in Eastern India out of which three locations were irrigated and one location was rainfed. The ECHAM climate scenario, Model for Interdisciplinary Research on Climate (MIROC)3.0 climate scenario, and ensemble models showed different levels of yield increase with a clear reduction in yield under rainfed rice as compared to irrigated rice. A distinct regional and cultivar difference in response of rice yield to elevated CO2 was seen in this study. Results obtained by simulation modeling at different climate change scenarios support the hypothesis that rice plant responses to elevated CO2 are through stimulation of photosynthesis. Realization of higher yields is linked with source sink dynamics and partitioning of assimilates wherein sink capacity plays an important role under elevated CO2 conditions.

Development of weather-based prediction models for leaf rust in wheat in the Indo-Gangetic plains of India

Weather based prediction models for leaf rust were developed using disease severity and weather data recorded at four locations viz. Ludhiana, Kanpur, Faizabad and Sabour of the All India Wheat and Barley Improvement Project. Weeks 7–9 of the crop growing season at Ludhiana, Faizabad and Sabour and weeks 10–12 at Kanpur were identified as critical periods for relating weather variables to disease. Highly significant correlation coefficients were found between disease severity and a greater number of weather variables in these critical 3-week periods than at other times. The correlation coefficients were greatest for the Humid Thermal Ratio (HTR), Maximum Temperature (MXT) and Special Humid Thermal Ratio (SHTR), and these three weather variables were selected as predictor variables. Linear regressions with these predictor variables (individually) during the critical periods, and a multiple regression with MXT and relative humidity (RH), serve as four disease prediction models, with sufficient lead-time to take control measures. Validation of these prediction models with independent disease severity data showed that the regression equation with MXT (Model-1) was the best among the prediction models, with four out of six simulations matching observed disease severity classes and also having lowest residual sum of squares (SSE) value of 2727. Models 4 (multiple regression), 2 (HTR) and 3 (SHTR) with SSE values of 2881, 3092 and 3732, respectively are in order of decreasing accuracy of prediction. The model using MXT can be used to predict the disease severity in the Indo-Gangetic Plains and provide the basis for efficient disease control.Weather based prediction models for leaf rust were developed using disease severity and weather data recorded at four locations viz. Ludhiana, Kanpur, Faizabad and Sabour of the All India Wheat and Barley Improvement Project. Weeks 7–9 of the crop growing season at Ludhiana, Faizabad and Sabour and weeks 10–12 at Kanpur were identified as critical periods for relating weather variables to disease. Highly significant correlation coefficients were found between disease severity and a greater number of weather variables in these critical 3-week periods than at other times. The correlation coefficients were greatest for the Humid Thermal Ratio (HTR), Maximum Temperature (MXT) and Special Humid Thermal Ratio (SHTR), and these three weather variables were selected as predictor variables. Linear regressions with these predictor variables (individually) during the critical periods, and a multiple regression with MXT and relative humidity (RH), serve as four disease prediction models, with sufficient lead-time to take control measures. Validation of these prediction models with independent disease severity data showed that the regression equation with MXT (Model-1) was the best among the prediction models, with four out of six simulations matching observed disease severity classes and also having lowest residual sum of squares (SSE) value of 2727. Models 4 (multiple regression), 2 (HTR) and 3 (SHTR) with SSE values of 2881, 3092 and 3732, respectively are in order of decreasing accuracy of prediction. The model using MXT can be used to predict the disease severity in the Indo-Gangetic Plains and provide the basis for efficient disease control.

Sensitive growth stages and temperature thresholds in wheat ( Triticum aestivum L.) for index-based crop insurance in the Indo-Gangetic Plains of India

High temperature stress at critical growth stages is a major risk factor for wheat in many wheat growing areas globally. Developing weather indices relating to yield reductions in wheat is an urgent requirement for weather-index-based crop insurance. The objectives of the present study were to: (i) identify critical phenological stage(s) for heat stress, (ii) quantify the impact of heat stress at critical growth stage(s) and (iii) work out thresholds of temperature for obtaining above average, average and below average yield of wheat. For achieving these objectives, 11 years’ experimental data for three cultivars (HD-2285, K-8804 and K-9107) under three sowing dates at the Kanpur Centre located in the Indo-Gangetic Plains of Uttar Pradesh, India were used. Among the eight phenological stages, the milk stage (growth stage 73) was identified as most sensitive for high maximum and minimum temperatures to adversely affect yield. The rate of yield reduction with unit increase in maximum and minimum temperatures (°C) was found to be highest in K-8804 and lowest in HD-2285. The optimum ranges of maximum temperature during anthesis, milk, dough and maturity stages are 19·7–21·9, 24·2–26·5, 26·1–28·8 and 29·5–30·8 °C, respectively and those for minimum temperature are 4·3–6·2, 8·3–9·7, 11·5–12·4 and 13·0–15·1 °C, respectively. The thresholds of temperature during critical stages and quantification of heat stress on yield will be of use in devising weather-index-based crop insurance products in wheat and also for breeding temperature-stress-resistant genotypes. This method of devising weather indices in the present study can be used in other crops and regions of the world as an adaptation strategy for climate change.

Effects of Organic and Inorganic Fertilizers on Soil and Plant Nutrients and Yields of Pearl Millet and Wheat under Semi-arid Inceptisols in India

A study was conducted to assess fertilizer effect on pearl millet–wheat yield and plant-soil nutrients with the following treatments: T1, control; T2, 100% nitrogen (N); T3, 100% nitrogen and phosphorus (NP); T4, 100% nitrogen, phosphorus and potassium (NPK); T5, 100% NPK + zinc sulfate (ZnSO4) at 25 kg ha−1; T6, 100% NPK + farmyard manure (FYM) at 10 t ha−1; T7, 100% NPK+ verimcompost (VC) at 2.5 tha−1; T8, 100% NPK + sulfur (S) at 25 kg ha−1; T9, FYM at 10 t ha−1; T10, VC at 2.5 t ha−1; T11, 100% NPK + FYM at 10 t ha−1 + 25 kg S ha−1 + ZnSO4 at 25 kg ha−1; and T12, 150% NPK treatments. Treatments differed significantly in influencing soil-plant nutrients and grain and straw yields of both crops. Grain yield had significant correlation with soil-plant N, P, K, S, and zinc (Zn) nutrients. The study indicated superiority of T11 for attaining maximum pearl millet grain yield (2885 kg ha−1) and straw yield (7185 kg ha−1); amounts of N (48.9 kg ha−1), P (8.8 kg ha−1), K (26.3 kg ha−1), S (20.6 kg ha−1), and Zn (0.09 kg ha−1) taken up; and amounts of soil N (187.7 kg ha−1), P (13.7 kg ha−1), K (242.5 kg ha−1), S (10.1 kg ha−1), and Zn (0.70 kg ha−1). It was superior for wheat with grain yield (5215 kg ha−1) and straw yield (7220 kg ha−1); amounts of N (120.7 kg ha−1), P (13.8 kg ha−1), K (30 kg ha−1), S (14.6 kg ha−1), and Zn (0.18 kg ha−1) taken up; and maintaining soil N (185.7 kg ha−1), P (14.5 kg ha−1), K (250.5 kg ha−1), S (10.6 kg ha−1), and Zn (0.73 kg ha−1). Based on the study, 100% NPK + FYM at 10 tha−1 + Zn at 25 kg ha−1 + S at 25 kg ha−1 could be recommended for attaining maximum returns of pearl millet–wheat under semi-arid Inceptisols.