Pramod K. Aggarwal

Climate Change and Rice Yields in Diverse Agro-Environments of India. II. Effect of Uncertainties in Scenarios and Crop Models on Impact Assessment

Estimates of impact of climate change on crop production could be biased depending upon the uncertainties in climate change scenarios, region of study, crop models used for impact assessment and the level of management. This study reports the results of a study where the impact of various climate change scenarios has been assessed on grain yields of irrigated rice with two popular crop simulation models- Ceres-Rice and ORYZA1N at different levels of N management. The results showed that the direct effect of climate change on rice crops in different agroclimatic regions in India would always be positive irrespective of the various uncertainties. Rice yields increased between 1.0 and 16.8% in pessimistic scenarios of climate change depending upon the level of management and model used. These increases were between 3.5 and 33.8% in optimistic scenarios. At current as well as improved level of management, southern and western parts of India which currently have relatively lower temperatures compared to northern and eastern regions, are likely to show greater sensitivity in rice yields under climate change. The response to climate change is small at low N management compared to optimal management. The magnitude of this impact can be biased upto 32% depending on the uncertainty in climate change scenario, level of management and crop model used. These conclusions are highly dependent on the specific thresholds of phenology and photosynthesis to change in temperature used in the models. Caution is needed in using the impact assessment results made with the average simulated grain yields and mean changes in climatic parameters.

Agricultural biotechnology for crop improvement in a variable climate: Hope or hype?

Developing crops that are better adapted to abiotic stresses is important for food production in many parts of the world today. Anticipated changes in climate and its variability, particularly extreme temperatures and changes in rainfall, are expected to make crop improvement even more crucial for food production. Here, we review two key biotechnology approaches, molecular breeding and genetic engineering, and their integration with conventional breeding to develop crops that are more tolerant of abiotic stresses. In addition to a multidisciplinary approach, we also examine some constraints that need to be overcome to realize the full potential of agricultural biotechnology for sustainable crop production to meet the demands of a projected world population of nine billion in 2050.

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Analyzing the limitations set by climatic factors, genotype, and water and nitrogen availability on productivity of wheat II. Climatically potential yields and management strategies

Abstract The objectives of this paper are to establish the climatically determined potential grain yields of wheat for different locations of India, to quantify the gap between actual and potential yields and to determine the optimal levels of irrigation and N required for given productivity levels. The analysis is based on simulations made with the crop growth model wtgrows . Simulated potential grain yields, determined by solar radiation and temperature, varied between 2.56 and 8.25 t ha −1 fot 138 locations spread throughout India. In general, yields increased with latitude and inland locations had greater yields than the coastal locations at the same latitude. These trends were related to mean temperature differnces iver latitude/location. The results indicate a strong linear decline in grain in yield as mean temperature increased. Late sowings had smaller yields as well as increased variability. The decrease for each days delay in sowing was more when potential yield was high. The yield gap was at least 2 t ha −1 irrespective of location and a significant portion of this was due to delayed sowing. Crop simulation with different amounts of nitrogen and irrigation inputs showed significant interaction between water and N availability as well as inter-seasonal climatic variability, particularly with low input of water. The optimal N application depended on the amount of water availability. Yield variance of stressed wheat crops is moderated greatly by irrigation but reduced N fertilizer appliction may modify the response.

Uncertainties in crop, soil and weather inputs used in growth models: Implications for simulated outputs and their applications

Abstract Deterministic crop growth models require several inputs relating to crop/variety, soil physical properties, weather and crop management. The input values used could be significantly uncertain due to random and systematic measurement errors and spatial and temporal variation observed in many of these inputs. Often soil and weather data are approximated using GIS and/or weather generators. In this paper total uncertainty in simulated yield, evapotranspiration and crop N uptake has been quantified considering uncertainties in crop, soil and weather inputs. WTGROWS, a crop model that simulates the effect of genotypic, climatic, edaphic and management factors on productivity of spring wheat was used. The uncertainty in each input was represented by a statistical distribution of values based on literature review, actual measurement and subjective expert judgement. The Monte Carlo simulation technique was used to analyze total uncertainty. The results showed that uncertainties in crop, soil and weather inputs resulted in uncertainty in simulated grain yield, ET and N uptake, which varied depending upon the production environment. Uncertainties in outputs increased as the production system changed from a potential production level to a level where crop growth was constrained by limited availability of water and nitrogen. There was an 80% probability that the bias in the deterministic model outputs was always less than 10% in potential and irrigated production systems. In rainfed environments this bias was larger. The bias in simulated outputs was less than or equal to model error. Most of the uncertainty in outputs caused by variable soil, crop and weather inputs could be represented if the outputs were determined using fixed soil and crop data, and a large series of weather data. In potential and irrigated production systems, inputs relating to crop photosynthesis and leaf area estimation had a large ‘uncertainty importance’. Uncertainties in soil N inputs and vapor pressure were also of great importance in irrigated environments. In rainfed environments, uncertainties in soil and weather inputs were dominant and crop parameters had only limited ‘uncertainty importance’. The implications of these results in estimates of potential and rainfed productivity, database development and guiding refinement of models are discussed.

Modelling the quantitative evaluation of soil nutrient supply, nutrient use efficiency, and fertilizer requirements of wheat in India

Wheat yields in many parts of India are stagnant. The main reason forthis is conventional blanket fertilizer recommendation, lower fertilizer useefficiency, and imbalanced use of fertilizers. Estimation of fertilizerrequirements based on quantitative approaches can assist in improving wheatyields and increasing nutrient use efficiency. We used the QUEFTS (QUantitativeEvaluation of Fertility of Tropical Soils) model for estimation of nitrogen(N),phosphorus (P), and potassium (K) requirements and fertilizer recommendationsfor a target yield of wheat. The model considers the interactions of N, P, andK, and climate adjusted potential yield of the region. Published data fromseveral field experiments dealing with N, P, and K conducted during the years1970 to 1998 across wheat-growing environments of India, covering a wide rangeof soil and climatic conditions, were used to reflect the environmentalvariability. The relationships between indigenous N, P, and K supply and soilorganic carbon, Olsen P, and ammonium acetate-extractable K, respectively, wereestablished. The required N, P, and K accumulation in the plant for 1 tonnegrain yield was 23.1, 3.5, and 28.5 kg, respectively, suggestinganaverage NPK ratio in the plant dry matter of about 6.6:1:8.1. The constants forminimum and maximum accumulation (kg grain kg−1) of N (27 and60), P (162 and 390), and K (20 and 59) were derived as the standard modelparameters in QUEFTS for fertilizer recommendation for irrigated wheat in thetropical and subtropical regions of India. Relationships of apparent recoveryefficiencies of fertilizer N, P, and K with levels of their application werealso determined. The observed yields of wheat with different amounts of thesenutrients were in good agreement with the values predicted by the model,indicating that the model can be used for fertilizer recommendations.

CLIMATE CHANGE AND RICE YIELDS IN DIVERSE AGRO-ENVIRONMENTS OF INDIA. I. EVALUATION OF IMPACT ASSESSMENT MODELS

This paper reports results of a comparison of two popular rice growth models- Ceres-Rice and ORYZA1N for the same input conditions. Both models use different approaches for simulating growth and yield, are sensitive to climate change parameters, and represent two major schools of crop modelling. A dataset of 32 experiments consisting of 98 treatments was assembled from an extensive literature search. These experiments were conducted over the period of 1980–1993 in diverse Indian locations from 11° N–33° N. The treatments varied in N management, sowing dates, varieties and seasons. The flowering duration in the dataset varied between 37 and 86 days and grain yields between 2587 kg ha−1 and 8877 kg ha−1. Seven treatments from this dataset, one for each variety, were selected for calibration. The genetic coefficients of different varieties used in the analysis for both models were estimated from this short-listed dataset by repeated iterations until a close match between simulated and observed phenology and yield was obtained in these treatments. Similarly 11 treatments with zero or low N applications were used for calibration of initial soil N for different locations. The remaining 80 treatments were used for validation of the models. Both models predicted satisfactorily the trends of leaf area and dry matter growth, grain number, days to flowering and grain yields. Simulated yields were within +15% of the measurements. Considering that the field measurements also generally have 10–15% error and that the treatments widely varied in weather conditions, particularly in temperature, it was concluded that both models are adequate to simulate the effects of climate change on rice yields in diverse agro-environments of India that are free from all pests.

Analyzing the limitations set by climatic factors, genotype, water and nitrogen availability on productivity of wheat I. The model description, parametrization and validation

Abstract A mechanistic crop growth simulation model, wtgrows , is developed for use in analyzing effects of climatic variables and crop management on productivity of wheat in tropical and sub-tropical wheat regions of India. The model, written in csmp and fse , simulates daily dry matter production as a function of radiation and temperature, and water and nitrogen availability. Crop aspects of the model are arranged in submodels covering development, photosynthesis, respiration, carbohydrate partitioning, dry matter production, leaf area, grain growth and transpiration. A soil water balance model is attached to simulate water uptake and to determine water stress. Another submodel determines nitrogen uptake, distribution and N stress. Water and nitrogen stresses, depending upon their severity, affect various physiological processes. The model requires inputs relating to site, daily weather, soil physical characteristics and crop management. Switches allow water and/or nitrogen stresses to be terminated to establish climatically determined potential grain yield. Various aspects of the model were validated using a large number of independent experiments. Comparison of simulated and measured quantities indicated satisfactory performance of the model in reference to water and nitrogen uptake, dry matter growth and grain yield in potential as well as Water- and N-limited environments. The model appears useful as a tool for optimizing use of water and nitrogen.

Performance of wheat and triticale cultivars in a variable soil—water environment II. Evapotranspiration, water use efficiency, harvest index and grain yield

Abstract This communication, based on a 3-year field study, describes the drought susceptibility of 17 cultivars of wheat and triticale, expressed in terms of grain yield, harvest index, pre- and post-anthesis evapotranspiration (ET) and water use efficiency (WUE). At the time of sowing, the available water (between−0.02 MPa and −1.5 MPa) in the soil profile varied between 12.7 and 17.6 cm depending upon the year. A large proportion of this water was consumed by the plants before anthesis, in the unirrigated treatment, leaving on average only 4.2 cm water in the initial 150-cm soil profile. However, precipitation in the post-anthesis phase increased the amount of available water. At crop maturity the soil profile had almost no available water. There were no differences in ET or WUE among the varieties studied. Grain yield and harvest index increased with increase in ET up to a certain level. Whereas the proportion of water used in the post-anthesis phase was important in irrigated plants, both pre- and post-anthesis water use were important in unirrigated wheat. Increase in ET decreased WUE but increased the harvest index. No relationship could be deduced between drought susceptibility and pre- and post-anthesis water use.

Understanding gender dimensions of agriculture and climate change in smallholder farming communities

In Uganda, Ghana and Bangladesh, participatory tools were used for a socio-economic and gender analysis of three topics: climate-smart agriculture (CSA), climate analogue approaches, and climate and weather forecasting. Policy and programme-relevant results were obtained. Smallholders are changing agricultural practices due to observations of climatic and environmental change. Women appear to be less adaptive because of financial or resource constraints, because of male domination in receiving information and extension services and because available adaptation strategies tend to create higher labour loads for women. The climate analogue approach (identifying places resembling your future climate so as to identify potential adaptations) is a promising tool for increasing farmer-to-farmer learning, where a high degree of climatic variability means that analogue villages that have successfully adopted new CSA practices exist nearby. Institutional issues related to forecast production limit their credibility and salience, particularly in terms of womens ability to access and understand them. The participatory tools used in this study provided some insights into womens adaptive capacity in the villages studied, but not to the depth necessary to address womens specific vulnerabilities in CSA programmes. Further research is necessary to move the discourse related to gender and climate change beyond the conceptualization of women as a homogenously vulnerable group in CSA programmes.