Overview of Crop Models for Assessment of Crop Production

Author(s):  
Joe T. Ritchie ◽  
Gopal Alagarswamy
Keyword(s):  
2019 ◽  
Author(s):  
Matias Heino ◽  
Joseph H. A. Guillaume ◽  
Christoph Müller ◽  
Toshichika Iizumi ◽  
Matti Kummu

Abstract. Climate oscillations are periodically fluctuating oceanic and atmospheric phenomena, which are related to variations in weather patterns and crop yields worldwide. In terms of crop production, the most widespread impacts have been observed for the El Niño Southern Oscillation (ENSO), which has been found to impact crop yields in all continents that produce crops, while two other climate oscillations – the Indian Ocean Dipole (IOD) and the North Atlantic Oscillation (NAO) – have been shown to impact crop production especially in Australia and Europe, respectively. In this study, we analyse the impacts of ENSO, IOD and NAO on the growing conditions of maize, rice, soybean and wheat at the global scale, by utilizing crop yield data from an ensemble of global gridded crop models simulated for a range of crop management scenarios. Our results show that simulated crop yield variability is correlated to climate oscillations to a wide extent (up to almost half of all maize and wheat harvested areas for ENSO) and in several important crop producing areas, e.g. in North America (ENSO, wheat), Australia (IOD & ENSO, wheat) and northern South America (ENSO, soybean). Further, our analyses show that higher sensitivity to these oscillations can be observed for rainfed, and fully fertilized scenarios, while the sensitivity tends to be lower if crops are fully irrigated. Since, the development of ENSO, IOD and NAO can be reliably forecasted in advance, a better understanding about the relationship between crop production and these climate oscillations can improve the resilience of the global food system to climate related shocks.


Water ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 1153 ◽  
Author(s):  
Qianxi Shen ◽  
Risheng Ding ◽  
Taisheng Du ◽  
Ling Tong ◽  
Sien Li

Water shortage is a main limitation of crop growth and yield in drought northwest China, which is an important area of seed maize growth. Plastic film mulch is widely adopted to reduce soil evaporation (E) and conserve water resources, which changes evapotranspiration (ET) and its components, E and transpiration (Tr) and crop growth. The AquaCrop model, one of widely used crop models powered by water, can well simulate crop ET components and growth. However, there are few studies that examine ET partitioning and growth with and without plastic film mulch. The calibrated AquaCrop model was used to partition ET and simulate growth of seed maize with and without plastic film mulch in a drought region of northwest China in 2014 and 2015. The AquaCrop model can well simulate canopy cover curve (CC), and the dynamic and accumulated courses of ET and ET components. Plastic film mulch could advance the growth stage of seed maize and reduce seasoned ET. The initial stage with plastic film mulch was 37–42 days, while it was 46–48 days for no-mulch. Plastic film mulch increased Tr by 14.16% and 14.48% and significantly decreased E by 57.25% and 34.28% in 2014 and 2015, respectively, resulting in the reduction of seasonal total ET. Plastic film mulch increased averaged mid-season crop coefficient for transpiration (Kc Tr) by 0.88% and decreased soil evaporation coefficient (Ke) by 62.50%. Collectively, the results suggest that, in comparison with no-mulch, plastic film mulch advanced crop growth, and decreased total ET and increased Tr related with crop production, i.e., improve water use effectiveness.


2010 ◽  
Vol 148 (6) ◽  
pp. 639-656 ◽  
Author(s):  
M. TRNKA ◽  
J. EITZINGER ◽  
M. DUBROVSKÝ ◽  
D. SEMERÁDOVÁ ◽  
P. ŠTĚPÁNEK ◽  
...  

SUMMARYThe reality of climate change has rarely been questioned in Europe in the last few years as a consensus has emerged amongst a wide range of national to local environmental and resource policy makers and stakeholders that climate change has been sufficiently demonstrated in a number of sectors. A number of site-based studies evaluating change of attainable yields of various crops have been conducted in Central Europe, but studies that evaluate agroclimatic potential across more countries in the region are rare. Therefore, the main aim of the present study was to develop and test a technique for a comprehensive evaluation of agroclimatic conditions under expected climate conditions over all of Central Europe with a high spatial resolution in order to answer the question posed in the title of the paper ‘Is rainfed crop production in central Europe at risk?’ The domain covers the entire area of Central Europe between latitudes 45° and 51·5°N and longitudes 8° and 27°E, including at least part of the territories of Austria, the Czech Republic, Germany, Hungary, Poland, Romania, Slovakia, Switzerland and Ukraine. The study is based on a range of agroclimatic indices that are designed to capture complex relations existing between climate and crops (their development and/or production) as well as the agrosystems as a whole. They provide information about various aspects of crop production, but they are not meant to compete with other and sometimes more suitable tools (e.g. process-based crop models, soil workability models, etc.). Instead, the selected indices can be seen as complementary to crop modelling tools that describe aspects not fully addressed or covered by crop models for an overall assessment of crop production conditions. The set of indices includes: sum of effective global radiation, number of effective growing days, Huglin index, water balance during the period from April to June (AMJ) and during the summer (JJA), proportion of days suitable for harvesting of field crops in June and July, and proportion of days suitable for sowing in early spring as well as during the autumn. The study concluded that while the uncertainties about future climate change impacts remain, the increase in the mean production potential of the domain as a whole (expressed in terms of effective global radiation and number of effective growing days) is likely a result of climate change, while inter-annual yield variability and risk may also increase. However, this is not true for the Pannonian (the lowlands between the Alps, the Carpathian Mountains and the Dinaric Alps) and Mediterranean parts of the domain, where increases in the water deficit will further limit rainfed agriculture but will probably lead to an increase in irrigation agriculture if local water resources are dwindling. Increases in the severity of the 20-year drought deficit and more substantial water deficits during the critical part of the growing season are very likely over the central and western part of the domain. Similarly, the inter-annual variability of water balance is likely to increase over the domain. There is also a chance of conditions for sowing during spring deteriorating due to unfavourable weather, which might increase the preference given to winter crops. This is already likely due to their ability to withstand spring drought stress events. Harvesting conditions in June (when harvest of some crops might take place in the future) are not improving beyond the present level, making the planning of the effective harvest time more challenging. Based on the evidence provided by the present study, it could be concluded that rainfed agriculture might indeed face more climate-related risks, but the overall conditions will probably allow for acceptable yield levels in most seasons. However, the evidence also suggests that the risk of extremely unfavourable years, resulting in poor economic returns, is likely to increase.


2005 ◽  
Vol 360 (1463) ◽  
pp. 2049-2065 ◽  
Author(s):  
Richard A. Betts

This paper discusses the need for a more integrated approach to modelling changes in climate and crops, and some of the challenges posed by this. While changes in atmospheric composition are expected to exert an increasing radiative forcing of climate change leading to further warming of global mean temperatures and shifts in precipitation patterns, these are not the only climatic processes which may influence crop production. Changes in the physical characteristics of the land cover may also affect climate; these may arise directly from land use activities and may also result from the large-scale responses of crops to seasonal, interannual and decadal changes in the atmospheric state. Climate models used to drive crop models may, therefore, need to consider changes in the land surface, either as imposed boundary conditions or as feedbacks from an interactive climate–vegetation model. Crops may also respond directly to changes in atmospheric composition, such as the concentrations of carbon dioxide (CO 2 ), ozone (O 3 ) and compounds of sulphur and nitrogen, so crop models should consider these processes as well as climate change. Changes in these, and the responses of the crops, may be intimately linked with meteorological processes so crop and climate models should consider synergies between climate and atmospheric chemistry. Some crop responses may occur at scales too small to significantly influence meteorology, so may not need to be included as feedbacks within climate models. However, the volume of data required to drive the appropriate crop models may be very large, especially if short-time-scale variability is important. Implementation of crop models within climate models would minimize the need to transfer large quantities of data between separate modelling systems. It should also be noted that crop responses to climate change may interact with other impacts of climate change, such as hydrological changes. For example, the availability of water for irrigation may be affected by changes in runoff as a direct consequence of climate change, and may also be affected by climate-related changes in demand for water for other uses. It is, therefore, necessary to consider the interactions between the responses of several impacts sectors to climate change. Overall, there is a strong case for a much closer coupling between models of climate, crops and hydrology, but this in itself poses challenges arising from issues of scale and errors in the models. A strategy is proposed whereby the pursuit of a fully coupled climate–chemistry–crop–hydrology model is paralleled by continued use of separate climate and land surface models but with a focus on consistency between the models.


2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Priscilla Ntuchu Kephe ◽  
Kingsley Kwabena Ayisi ◽  
Brilliant Mareme Petja

AbstractA broad scope of crop models with varying demands on data inputs is being used for several purposes, such as possible adaptation strategies to control climate change impacts on future crop production, management decisions, and adaptation policies. A constant challenge to crop model simulation, especially for future crop performance projections and impact studies under varied conditions, is the unavailability of reliable historical data for model calibrations. In some cases, available input data may not be in the quantity and quality needed to drive most crop models. Even when a suitable choice of a crop simulation model is selected, data limitations hamper some of the models’ effective role for projections. To date, no review has looked at factors inhibiting the effective use of crop simulation models and complementary sources for input data in South Africa. This review looked at the barriers to crop simulation, relevant sources from which input data for crop models can be sourced, and proposed a framework for collecting input data. Results showed that barriers to effective simulations exist because, in most instances, the input data, like climate, soil, farm management practices, and cultivar characteristics, were generally incomplete, poor in quality, and not easily accessible or usable. We advocate a hybrid approach for obtaining input data for model calibration and validation. Recommended methods depending on the intended outputs and end use of model results include remote sensing, field, and greenhouse experiments, secondary data, engaging with farmers to model actual on-farm conditions. Thus, employing more than one method of data collection for input data for models can reduce the challenges faced by crop modellers due to the unavailability of data. The future of modelling depends on the goodness and availability of the input data, the readiness of modellers to cooperate on modularity and standardization, and potential user groups’ ability to communicate.


2020 ◽  
Author(s):  
Shannon de Roos ◽  
Gabriëlle de Lannoy ◽  
Dirk Raes

<p>The pressure on soil and water resources to support the demand for crop production calls for effective water management at the regional scale and a need for regional crop models.</p><p>In our study, the field-based Aquacrop v.6.1 is modified to a gridded crop model that is run spatially over the main part of Europe at 1-km resolution.</p><p>The gridded model simulates spatially distributed soil moisture, crop biomass and yield, given spatial input of meteorological forcings extracted from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and 1-km soil texture information from the Harmonized World Soil Database v1.2 (HWSD v1.2). For the first model evaluation, a hypothetical and uniform crop is implemented, and field management and irrigation practices are not included. We will present preliminary results over Europe by comparing the spatial soil moisture and biomass simulations with remote sensing data.</p><p>This work is part of the SHui project, a H2020 project that aims at improving stakeholder decision-making for water scarcity management in European and Chinese cropping systems.</p>


2000 ◽  
Vol 134 (2) ◽  
pp. 173-180
Author(s):  
E. KEBREAB ◽  
J. FRANCE ◽  
R. H. ELLIS ◽  
C. GARFORTH

In the last two decades, crop production models have been developed or modified for use in the semi-arid tropics. Although potential uses of crop models have been discussed in detail in the literature, examples of successful uptake and impact of those models is lacking. Four models developed specifically for the semi-arid tropics were used as a basis for evaluating uptake and impact of models in the semi-arid tropics. PARCH accounts for differences in water availability when predicting yield. PARCHED-THIRST covers water-harvesting, run-off and run-on. EMERGE identifies opportunities for successful crop establishment, and SWEAT calculates evapo-transpiration and estimates temperature and moisture throughout the soil profile. The models are dynamic, deterministic and mechanistic in nature. The equations and notations comprising them are generally well structured, meaningful and concise. The uptake and impact of these models on crop production in the semi-arid tropics was assessed using questionnaires and semi-structured interviews with the model developers. There was limited uptake. Low uptake resulted from lack of efficient dissemination and discontinuity in information transfer: from model developers to scientists in the national research institutions; and thereon to extension agents and so to farmers. Although this paper is based on a study of only four models, there are important lessons to be drawn in order to avoid similar mistakes being repeated. Guidelines for improving impact for future crop production modelling projects are proposed.


Author(s):  
João Vasco Silva ◽  
Ken E. Giller

Abstract Crop production is at the core of a ‘perfect storm’ encompassing the grand challenges of achieving food and nutrition security for all, in the face of climate change, while avoiding further conversion of natural habitats for agriculture and loss of biodiversity. Here, we explore current trends in crop modelling related to these grand challenges by reflecting on research presented at the Second International Crop Modelling Symposium (iCropM2020). A keyword search in the book of abstracts of the symposium revealed a strong focus on ‘climate change’, ‘adaptation’ and ‘impact assessment’ and much less on ‘food security’ or ‘policy’. Most research focused on field-level investigations and far fewer on farm(ing) systems levels – the levels at which management decisions are made by farmers. Experimentation is key to development and testing of crop models, yet the term ‘simulation’ outweighed by far the terms ‘experiments’ and ‘trials’, and few contributions dealt with model improvement. Cereals are intensively researched, whereas roots, tubers and tropical perennials are under-researched. Little attention is paid to nutrient limitations apart from nitrogen or to pests and diseases. The aforementioned aspects represent opportunities for future research where crop models can help in devising hypotheses and driving new experimentation. We must also ensure that crop models are fit for their intended purposes, especially if they are to provide advice to policymakers. The latter, together with cross-scale and interdisciplinary efforts with direct engagement of stakeholders are needed to address the grand challenges faced by food and agricultural systems in the next century.


2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Xuhui Wang ◽  
Christoph Müller ◽  
Joshua Elliot ◽  
Nathaniel D. Mueller ◽  
Philippe Ciais ◽  
...  

AbstractIrrigation is the largest sector of human water use and an important option for increasing crop production and reducing drought impacts. However, the potential for irrigation to contribute to global crop yields remains uncertain. Here, we quantify this contribution for wheat and maize at global scale by developing a Bayesian framework integrating empirical estimates and gridded global crop models on new maps of the relative difference between attainable rainfed and irrigated yield (ΔY). At global scale, ΔY is 34 ± 9% for wheat and 22 ± 13% for maize, with large spatial differences driven more by patterns of precipitation than that of evaporative demand. Comparing irrigation demands with renewable water supply, we find 30–47% of contemporary rainfed agriculture of wheat and maize cannot achieve yield gap closure utilizing current river discharge, unless more water diversion projects are set in place, putting into question the potential of irrigation to mitigate climate change impacts.


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