M.J. Kropff

On approaches and applications of the Wageningen crop models

Abstract Since the pioneering work of C.T. de Wit in the 1960s, the Wageningen group has built a tradition in developing and applying crop models. Rather than focusing on a few models, diversity is its trademark. Here we present an overview of the Wageningen crop and crop-soil modelling approaches along three criteria. The first criterion relates to the production situations the models are dealing with (i.e. potential, water and/or nutrient-limited, and actual production situations including pests, diseases and weeds). Second, models differ as a result of the objectives of model development, and hence required scale and degree of detail and comprehensiveness. Third, models have at least three potential application domains, i.e. research, education and support of learning and decision making processes. We describe both summary and more comprehensive modelling approaches for the major production situations. An overview of most of the Wageningen models is presented together with a more detailed description of LINTUL, SUCROS, ORYZA, WOFOST and INTERCOM. Illustrations for each of the three application domains are presented, i.e. plant type design, guiding experimental research, education, yield gap analysis, evaluation of manure policies, crop growth monitoring system and analysis and design of farming and regional land use systems. We discuss common issues of model verification, model validation, model validity and data requirements, and present information on software implementation, model and software documentation and distribution policy. Finally, we reflect upon the Wageningen modelling approaches and identify a number of key issues for future research. Major achievements of Wageningen modelling efforts include (1) a broad variety of approaches for modelling of systems at different scales and with different purposes; (2) their contribution to quantitative systems thinking in general, also for applications at higher hierarchical levels; (3) a strong linkage between crop modelling and higher education, both at undergraduate, graduate and post-doctoral level. To continually increase our understanding of crops and production systems a diversified approach must be cherished. At the same time we conclude that focus is required on a limited number of modules in a more integrated modelling framework for the benefit of analysing, evaluating and designing cropping systems. This review may be instrumental in the development of such an integrated framework.

A nonlinear model for crop development as a function of temperature

The Beta function, commonly used as a skewed probability density function in statistics, was introduced to describe the effect of temperature on the rate of crop development. The framework is set by three cardinal temperatures, namely the base (Tb), the optimum (To) and the ceiling (Tc) temperature. The model parameters Tb and Tc and three other coefficients μ, α and β can be used to derive the value of To and the maximum development rate. Parameter α also characterizes the curvature of the relationship with temperatures between Tb and To, and parameter β describes the curvature between To and Tc. The model has one parameter less than the Rice Clock Model (RCM); and in contrast to the RCM, it ensures that the maximum development rate occurs exactly at To. The model accurately described the response to temperature of several developmental processes, and was superior to two widely used thermal time approaches in predicting rice flowering time.

Simulating the impact of climate change on rice production in Asia and evaluating options for adaptation.

Abstract The likely effects of climate change caused by increasing atmospheric carbon dioxide levels on rice production in Asia were evaluated using two rice crop simulation models, ORYZA1 and SIMRIW, running under ‘fixed-change’ climate scenarios and scenarios predicted for a doubled-CO2 (2 × CO2) atmosphere by the General Fluid Dynamics Laboratory (GFDL), the Goddard Institute of Space Studies (GISS) and the United Kingdom Meteorological Office (UKMO) General Circulation Models. In general, an increase in CO2 level was found to increase yields while increases in temperature reduced yields. Overall rice production in the region was predicted by the ORYZA1 model to change by +6.5, −4.4 and −5.6% under the GFDL, GISS and UKMO 2×CO2 scenarios, respectively, while the corresponding changes predicted by the SIMRIW model were +4.2, −10.4 and −12.8%. The average of these estimates would suggest that rice production in the Asian region may decline by −3.8% under the climate of the next century. Declines in yield were predicted under the GISS and UKMO scenarios for Thailand, Bangladesh, southern China and western India, while increases were predicted for Indonesia, Malaysia, and Taiwan and parts of India and China. Modification of sowing dates at high latitudes, where warmer temperatures allowed a longer growing season, permitted a possible transition from single-cropping to double-cropping at some locations, an adaptation that could potentially have a large positive impact on national rice production in some countries. Planting dates could also be adjusted to avoid high temperatures at the time of flowering which can cause severe spikelet sterility in some varieties, although a delay in planting in some cases may prevent a second crop from being obtained because of high temperatures later in the season. Selection for varieties with a higher tolerance of spikelet fertility to temperature was shown to be capable of restoring yield levels to those predicted for current climates. The use of longer-maturing varieties to take advantage of longer growing seasons at higher latitudes may instead result in lower yields, due to the grain formation and ripening periods being pushed to less favorable conditions later in the season. A better strategy might be to select for shorter-maturing varieties to allow a second crop to be grown in these regions.

Future contributions of crop modelling—from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement

Crop modelling has evolved over the last 30 or so years in concert with advances in crop physiology, crop ecology and computing technology. Having reached a respectable degree of acceptance, it is appropriate to review briefly the course of developments in crop modelling and to project what might be major contributions of crop modelling in the future. Two major opportunities are envisioned for increased modelling activity in the future. One opportunity is in a continuing central, heuristic role to support scientific investigation, to facilitate decision making by crop managers, and to aid in education. Heuristic activities will also extend to the broader system-level issues of environmental and ecological aspects of crop production. The second opportunity is projected as a prime contributor in understanding and advancing the genetic regulation of plant performance and plant improvement. Physiological dissection and modelling of traits provides an avenue by which crop modelling could contribute to enhancing integration of molecular genetic technologies in crop improvement.

Drought-stress responses of two lowland rice cultivars to soil water status.

The physiological and morphological responses of two semi-dwarf lowland rice cultivars to transient drought were studied in three greenhouse experiments. These responses were related to root-zone soil water status for use in a rainfed-rice simulation model. Results were very similar for both varieties. Drought responses in young plants occurred at a lower soil water status than in older plants. The first observed effect in a drought period in the vegetative phase was a decline in leaf expansion rate compared to well-watered plants. Leaf expansion stopped completely with root-zone soil water pressure potential h in the range −50 to −250 kPa, depending on crop age and growing season. The rate of transpiration, corrected for differences in LAI, remained roughly equal to that of well-watered plants in the range 0 > h > −100 kPa, depending on crop age. As the soil water status declined further, relative transpiration rate decreased with increasing values of log(|h|), following a logistic function. Leaf rolling and early senesence started at h < −200 kPa or lower and were linearly related with log(|h|). Yield differences between plants that were transiently stressed in the early vegetative phase and well-watered plants were not significant. However, flowering and maturity were delayed. Severe drought in the reproductive phase resulted in large yield reductions, mainly caused by an increase in the percentage of unfilled grains and also in grain weight.

Physiology and modelling of traits in crop plants: implications for genetic improvement

Abstract Crop growth models have excellent potential for evaluating genetic improvement, for analyzing past genetic improvement from experimental data, and for proposing plant ideotypes for target environments. Crop models used for these plant breeding applications should be sufficiently mechanistic that processes can be investigated in a manner familiar to crop physiologists and plant breeders. In addition, the crop models must consider a sufficient number of cultivar-specific traits descriptive of life cycle phases, vegetative traits, and reproductive growth attributes. In this paper, we discuss how crop models consider genetic variability within a species (cultivar variation), how varietal characteristics can be determined from variety trial or other data, how crop models can be used to evaluate past genetic improvement, and how crop models can be used to hypothesize ideotypes for specific environments. We conclude that crop growth models can partially reproduce genotype by environment interactions when considered across broad ranges of weather and sites, and that crop models can be used to help plant breeders target cultivar improvement for specific environments. However, more physiological insight into primary processes such as source–sink relationships and morphological development will be needed for enhanced application of the models in breeding programmes.

Systems approaches for the design of sustainable agro-ecosystems

The complexity of agricultural systems and the need to fulfil multiple objectives in sustainable agro-ecosystems call for interdisciplinary analyzes and input from a wide variety of disciplines in order to better understand the complete agronomic production system. Systems approaches have been developed to support these interdisciplinary studies; their development and use have increased strongly in the past decades. Agronomic systems have pronounced spatial and temporal dimensions. Spatial aspects can be distinguished at crop, field, farm, regional and higher levels while processes at each spatial level have characteristic temporal components. Systems analysis in agronomic systems implies the use of various types of knowledge, such as expert knowledge including stakeholder expertise and knowledge derived from scientific measurements and model-simulations. The latter two can be derived from different types of studies: simple, rapid and cheap procedures, which are often relatively unreliable, at one end of the scale and complex, cumbersome and expensive data-intensive procedures at the other end. Selection of proper procedures for specific issues, both in terms of measurements and in applying simulation models, needs attention. Each problem requires its own research approach. Based on the output requirements and data availability, the proper systems approach has to be selected. Examples of these different procedures are given in this paper. Considering the type of problems to be studied in agronomic systems, different procedures can be followed to address the issues raised at a specific scale. These procedures start with a proper analysis of the system followed by studies that are projectory, exploratory, predictive, or are focused on decision support. Examples will be provided. Increasingly, systems approaches include stakeholders to fine-tune problem definition, the research itself, and the implementation of results. Stakeholders are farmers and citizens on farm and community levels and policy makers and planners at higher levels of aggregation. A comprehensive interdisciplinary analysis of agricultural production systems is seen as a necessary condition for the development of innovative, sustainable systems for the future. Systems for improving crop production systems are presented in this paper as well as applications of systems approaches at the farm and regional levels with emphasis on selecting the right approach.

AFLP mapping of quantitative trait loci for yield-determining physiological characters in spring barley

Abstract An amplified fragment length polymorphism (AFLP) map covering 965 cM was constructed using 94 recombinant inbred lines of a cross between the spring barley varieties Prisma and Apex. This map was employed to identify quantitative trait loci (QTLs) controlling plant height, yield and yield-determining physiological characters using an approximate multiple-QTL model, the MQM method. The seven physiological traits were parameters used in a process-based crop-growth model that predicts barley biomass production as affected by daily temperature and radiation. The traits were measured in experiments conducted over 2 years. Except for the relative growth rate of leaf area, all traits examined had at least one QTL in each year. QTLs and their effects were found to vary with developmental stages for one trait, the fraction of shoot biomass partitioned to leaves, that was measured at several stages. Most of the traits were associated, though to different extents, with the denso dwarfing gene (the height-reducing allele in Prisma) located on the long arm of chromosome 3. Some of the QTLs were mapped to similar positions in both years. The results in relation to effects of the dwarfing gene, the physiological basis for QTL×environment interaction, and the relative importance of the parameter traits with respect to yield, are discussed.

Applications of systems approaches at the field level

Preface. The Challenge of Integrating Systems Approach in Plant Breeding: Opportunities, Accomplishments, and Limitations P.K. Aggarwal, et al. Using Systems Approaches for Targeting Site Specific Management on Field Level J. Bouma, et al. New High-Yielding, Weed Competitive Rice Plant Types Drawing from O. sativa and O. glaberrima Genepools M. Dingkuhn, et al. Improving Rice Tolerance to Barnyardgrass through Early Crop Vigour: Simulations with INTERCOM J.L. Lindquist, M.J. Kropff. Recent Advances in Breeding for Drought Tolerance in Maize G.O. Edmeades, et al. Potential Yield of Irrigated Rice in African Arid Environments M. Dingkuhn, A. Sow. Assessing the Potential Yield of Tropical Crops: Role of Field Experimentation and Simulation R.C. Muchow, M.J. Kropff. Evaluation of the CROPGRO-Soybean Model over a Wide Range of Experiments K.J. Boote, et al. Adaptation of the CROPGRO Model to Simulate the Growth of Field-Grown Tomato J.M.S. Scholberg, et al. A Modified Version of CERES to Predict the Impact of Soil Water Excess on Maize Crop Growth and Development J.I. Kizaso, J.T. Ritchie. Mitigating Climate Change Effects on Rice Yield S. Mohandass, T.B. Ranganathan. Competition for Light in Windbreak-Millet Systems in the Sahel M. Mayus, et al. Crop Models and Precision Agriculture M.Y.L. Boone, et al. A Conceptual Model for Sodium Uptake and Distribution in Irrigated Rice F. Asch, et al. Using Decision Support Systems to Optimize Barley Management on Spatial Variable Soil H.W.G. Booltink, J. Verhagen. Application of SOYGRO in Argentina S. Meira, E. Guevara. Modeling the Effect of Nitrogen on Rice Growth and Development T. Hasegawa, T. Horie. Optimization of Nitrogen Fertilizer Application to Irrigated Rice R.N. Dash, et al. Simulating Rice Leaf Area Development and Dry Matter Production in Relation to Plant N and Weather M. Ohnishi, et al. Influence of Split Application of Nitrogen on Foliar N Content, Photosynthesis, Dry Matter Production and Yield in Short and Medium Duration Rice Cultivars C. Vijayalakshmi, M. Nagarajan. Systems Approaches to Improve Nitrogen Management in Rice B. Mishra. Use of Simulation Models to Optimize Fungicide Use for Managing Tropical Rice Blast Disease S.B. Calvero, P.S. Teng. Yield Gap Analysis of Rainfed Lowland Systems to Guide Rice Crop and Pest Management H.O. Pinnschmidt, et al. Quantification of the Effects of Bacterial Blight Disease on Rice Crop Growth and Grain Yield P.R. Reddy. Better Biological Control by a Combination of Experimentation and Modelling J.C. Van Lenteren, H.W.J. Van Roermund. Quantitative Evaluation of Growth and Yield of Rice Plants Infested with Rice Planthoppers T. Watanabe, et al. Addressing Sustainability of Rice--Wheat Systems: Analysis of Long-Term Experimentation and Simulation J. Timsina, et al. Systems Approach in the Design of Soil and Water Conservation Measures L. Stroosnijder, P. Kiepe. Farming Systems for Sustainable Agriculture and Environmental Quality R.S. Kanwar, et al. Harnessing Crop Research Data to Develop Expert Systems K. Muralidharan, E.A. Siddiq. Comparison of Predictions and Observations to Assess Model Performance: A Method of Empirical Validation P.L. Mitchell, J.E. Sheehy. Acronyms. Subject Index.

Use of ecophysiological models for crop-weed interference: relations amongst weed density, relative time of weed emergence, relative leaf area, and yield loss.

The performance of a mechanistic simulation model of crop-weed competition was evaluated with data on the effects of weed density, relative time of weed emergence, and environmental conditions on crop yield for three different crop-weed combinations. Reductions in crop yields due to weed competition were simulated accurately for all experiments, except for one case in which severe water stress combined with weed competi- tion altered crop morphological development (height and leaf area). The mechanistic model was then used to assess the potential and constraints of two empirical models of crop-weed competition, one based upon weed density and relative time of emergence, and the other on relative leaf area. The empirical model describing the relationship between relative leaf area of the weeds shortly after crop emergence and yield loss appeared to have several advantages for management applications, whereas the mechanistic model is more suited for research purposes. Additional index words. Simulation, interference, Zea mays L., Echinochloa crus-galli (L.) Beauv. #3 ECHCG, Beta vulgaris L., Chenopodium album L. #3 CHEAL, Lycopersi- con esculentum L., Solanum ptycanthum Dun. #3 SOLPT.