Ehsan Eyshi Rezaei

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Verified

Ehsan verified their affiliation via an institutional email.

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• Ph.D
• Working group lead at Leibniz Centre for Agricultural Landscape Research

Growing evidence suggests that the warming trend observed in many parts of the world has considerably modified crop phenology during the last decades but little is known about the impact of changes in crop management on crop phenology and possible interactions with temperature increase, and whether responses can be generalized across crop types. He...

The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat y...

Heat and drought stress can reduce crop yields considerably which is increasingly assessed with crop models for larger areas. Applying these models originally developed for the field scale at large spatial extent typically implies the use of input data with coarse resolution. Little is known about the effect of data resolution on the simulated impa...

Higher temperatures during the growing season are likely to reduce crop yields with implications for crop production and food security. The negative impact of heat stress has also been predicted to increase even further for cereals such as wheat under climate change. Previous empirical modeling studies have focused on the magnitude and frequency of...

Crop models are essential tools for assessing the threat of climate change to local and global food production1. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature2. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improveme...

Drought research addresses one of the major natural hazards that threatens progress toward the Sustainable Development Goals. This study aims to map the evolution and interdisciplinarity of drought research over time and across regions, offering insights for decision-makers, researchers, and funding agencies. By analysing more than 130 000 peer-rev...

This paper describes the dataset that was used to test the reliability of eight crop models in simulating growth and yield of canola in response to sowing dates, nitrogen inputs and climate variability across five countries. The dataset includes four spring cultivars and three winter cultivars across six sites, which represents a diverse range of c...

Given the negative impacts of climate change on crop production, it is vital to implement efficient adaptation and mitigation strategies. The diversification of cropping systems, particularly through intercropping combined with shifts in sowing times, could have the potential to offset such negative impacts. Yet, both experimental data and simulati...

Improving crop yield and stability is crucial for sustainable food production, which is predominantly influenced by climate. Nutrient management mitigates the negative impacts of climate change on yield stability, but little is known about the explanatory capability of climate variables (especially canopy, soil, and nighttime temperatures) and soil...

High-yielding traits can potentially improve yield performance under climate change. However, data for these traits are limited to specific field sites. Despite this limitation, field-scale calibrated crop models for high-yielding traits are being applied over large scales using gridded weather and soil datasets. This study investigates the implica...

This paper describes the data set that was used to test the accuracy of twenty-nine crop models in simulating the effect of changing sowing dates and sowing densities on wheat productivity for a high-yielding environment in New Zealand. The data includes one winter wheat cultivar (Wakanui) grown during six consecutive years, from 2012-2013 to 2017-...

Drought research addresses a major natural hazard with adverse impacts towards achieving the sustainable development goals. Here, we analyzed more than 130,000 peer-reviewed articles indexed in Scopus, spanning from 1901 to 2022 using a generative model. The results delineate distinct shifts in research priorities. Plant genetic research for drough...

Breeding advancements have significantly improved grain yield over recent decades, yet further progress is needed to meet global food demands amid a growing population. In this study, we systematically examine the responses of both historical and modern winter wheat cultivars, released between 1895 and 2007 in Germany, to varying management intensi...

Moving from sole cropping to intercropping is a transformative change in agriculture, contributing to several ecosystem services. However, modelling intercropping is challenging due to intensive parameterisation, complex calibration, and experiment scarcity. To facilitate future understanding, design and adaptation of intercropping, it is therefore...

To address the rising global food demand in a changing climate, yield gaps (Y G), the difference between potential yields under irrigated (Y P) or rainfed conditions (Y WL) and actual farmers' yields (Y a), must be significantly narrowed whilst raising potential yields. Here, we examined the likely impacts of climate change (including changes in cl...

Climate change challenges efforts to maintain and improve crop production in many regions. In this Review, we examine yield responses to warmer temperatures, elevated carbon dioxide and changes in water availability for globally important staple cereal crops (wheat, maize, millet, sorghum and rice). Elevated CO2 can have a compensatory effect on cr...

A non-destructive, convenient, and low-cost yield estimation at the field scale is vital for precision farming. Significant progress has been made in using UAV-based canopy features to predict crop yield during the mid-growth stages. However, there has been limited effort to explore yield estimation specifically after crop maturity. Researching the...

Increasing genetic wheat yield potential is considered as critical to increasing global wheat yields and production, baring major changes in consumption patterns. Climate change challenges breeding by making target environments less predictable, altering regional productivity and potentially increasing yield variability. Here we used a crop simulat...

Optical unmanned aerial vehicle (UAV) remote sensing is widely prevalent to estimate crop aboveground biomass (AGB). Nevertheless, limited knowledge of the UAV flight height (mainly characterized by different image numbers and spatial resolutions) influences the crop AGB estimation accuracy across diverse sensing datasets and machine/deep learning...

Grain production must increase by 60% in the next four decades to keep up with the expected population growth and food demand. A significant part of this increase must come from the improvement of staple crop grain yield potential. Crop growth simulation models combined with field experiments and crop physiology are powerful tools to quantify the i...

The impacts of climate change on food production in North Africa and Southwest Asia are severe, with rising temperatures and prolonged dry spells adversely affecting crop productivity and quality. Crop models are utilized to evaluate the impact of climate change and the potential of adaptation strategies to stabilize/enhance crop yields. The majori...

not yet peer-reviewed

The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchma...

Agricultural system analysis has considerably evolved over the last years, allowing scientists to quantify complex interactions in crops and agroecosystems. Computer-based models have become a central tool for such analysis, using formulated mathematical representations (algorithms) of different biophysical processes to simulate complex system beha...

Climate change and a rapidly increasing population boost the pressure on Türkiye's cropping systems to increase crop production in order to meet rising food demand. It is unknown whether and in which direction trends and variability in harvested area and yield separately affect crop production in Türkiye. The objective of this study was to (1) quan...

Accurate and in-time monitoring of cropping systems is critical to precision farming in order to facilitate decision-making for agronomic management and enhancing crop yield under changing climate. In this study, multi-source unmanned aerial vehicle (UAV) remote sensing observations were conducted at several key growing stages of crops at a standar...

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosy...

Plant density is a significant variable in crop growth. Plant density estimation by combining unmanned aerial vehicles (UAVs) and deep learning algorithms is a well-established procedure. However, flight companies for wheat density estimation are typically executed at early development stages. Further exploration is required to estimate the wheat p...

Most of the experimental and modeling studies that evaluate the impacts of climate change and variability on barley have been focused on grain yield. However, little is known on the effects of combined change in temperature, CO2 concentration, and extreme events on barley grain quality and how capable are the current process-based crop models captu...

With the occurrence of extreme events projected to increase under climate change, it is critical to assess the risk they pose to food security and identify suitable adaptation options. While mechanisms and impacts of climatic stressors (e.g. frost, drought, heat or flooding) have been studied individually, little is known their combined impacts on...

A c c e p t e d M a n u s c r i p t 3 Highlight An ensemble of 29 wheat crop models simulates seasonal wheat growth well under locally recommended sowing conditions, but needs improvements to capture the yield response to early sowing, especially under high sowing density. Abstract Crop multi-model ensembles (MME) have proven to be effective in inc...

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of a MME to capture crop response to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to cl...

Climate change will severely influence the yield, production and water demand of processing tomatoes. Atmospheric CO2 concentration may offset, but not fully compensate, the adverse effects of elevated temperatures.

Unmanned aerial vehicle (UAV) remote sensing and machine learning have emerged as a practical approach with ultra-high temporal and spatial resolutions to overcome the limitations of ground-based sampling for continuous crop monitoring. However, little is known on the suitability of distinct sensing indices for different crop management and distinc...

To better understand how climate change might influence global canola production, scientists from six countries have completed the first inter-comparison of eight crop models for simulating growth and seed yield of canola, based on experimental data from six sites across five countries. A sensitivity analysis was conducted with a combination of fiv...

Crop models are essential tools for analysing the effects of climate variability, change on crop growth and development and the potential impact of adaptation strategies. Despite their increasing usage, crop model estimations have implicit uncertainties which are difficult to classify and quantify. Failure to address these uncertainties may result...

Unmanned aerial vehicle (UAV)-based multispectral remote sensing effectively monitors agro-ecosystem functioning and predicts crop yield. However, the timing of the remote sensing field campaigns can profoundly impact the accuracy of yield predictions. Little is known on the effects of phenological phases on skills of high-frequency sensing observa...

This study investigates the main drivers of uncertainties in simulated irrigated maize yield under historical conditions as well as scenarios of increased temperatures and altered irrigation water availability. Using APSIM, MONICA, and SIMPLACE crop models, we quantified the relative contributions of three irrigation water allocation strategies, th...

The regular drought episodes in South Africa highlight the need to reduce drought risk by both policy and local community actions. Environmental and socioeconomic factors in South Africa's agricultural system have been affected by drought in the past, creating cascading pressures on the nation's agro-economic and water supply systems. Therefore, un...

Satellite and unmanned aerial vehicle (UAV) remote sensing can be used to estimate soil properties; however, little is known regarding the effects of UAV and satellite remote sensing data integration on the estimation of soil comprehensive attributes, or how to estimate quickly and robustly. In this study, we tackled those gaps by employing UAV mul...

Climate change, increasing environmental pollution, continuous loss of biodiversity, and a growing human population with increasing food demand, threaten the functioning of agro-ecosystems and their contribution to people and society. Agroforestry systems promise a number of benefits to enhance nature’s contributions to people. There are a wide ran...

The uncertainties associated with crop model inputs can affect the spatio-temporal variance of simulated yields, particularly under suboptimal irrigation. The aim of this study was to determine and quantify the main drivers of irrigated potato yield variance; as influenced by crop management practices as well as climate and soil factors. Using a lo...

Increasing population and a severe water crisis impose growing pressure on cropping systems of Iran to increase crop production meeting the rising demand for food. Little is known on the separate contribution of trends and variability of the harvested area and yield on crop production in severely drought-prone areas such as Iran. In this study, we...

Crop models were originally developed for application at the field scale but are increasingly used to assess the impact of climate and/or agronomic practices on crop growth and yield and water dynamics at larger scales. This raises the question of how data aggregation approaches affect outputs when using crop models at large spatial scales. This st...

One of the major sources of uncertainty in large-scale crop modeling is the lack of information capturing the spatiotemporal variability of crop sowing dates. Remote sensing can contribute to reducing such uncertainties by providing essential spatial and temporal information to crop models and improving the accuracy of yield predictions. However, l...

Yield stability is important for food security and a sustainable crop production, especially under changing climatic conditions. It is well known that the variability of yields is linked to changes in meteorological conditions. However, little is known about the long-term effects of agronomic management strategies, such as the supply of important n...

Drought is one of the extreme climatic events that has a severe impact on crop production and food supply. Our main goal is to test the suitability of remote sensing-based indices to detect drought impacts on crop production from a global to regional scale. Moderate resolution imaging spectroradiometer (MODIS) based imagery, spanning from 2001 to 2...

Crop models are powerful tools to explore agricultural impacts and adaptation to climate change. They are extensively used to predict the effect of climate change on agriculture. In this chapter, the authors review how crop models take into consideration climate variables and how they are used for climate change impact assessment studies, leading t...

Drought is a recurrent global phenomenon considered one of the most complex hazards with manifold impacts on communities, ecosystems, and economies. While many sectors are affected by drought, agriculture's high dependency on water makes it particularly susceptible to droughts, threatening the livelihoods of many, and hampering the achievement of t...

Input data aggregation affects crop model estimates at the regional level. Previous studies have focused on the impact of aggregating climate data used to compute crop yields. However, little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) on irrigation water requirement (IWR) in cool-temperate and spatially he...

Droughts continue to affect ecosystems, communities and entire economies. Agriculture bears much of the impact, and in many countries it is the most heavily affected sector. Over the past decades, efforts have been made to assess drought risk at different spatial scales. Here, we present for the first time an integrated assessment of drought risk f...

Input data aggregation influences crop model estimates at the regional level. Previous studies have focused on the impact of aggregating the climate data used to compute crop yields. Little is known about the combined data aggregation effect of climate (DAEc) and soil (DAEs) model inputs. This study explores the implications of using coarse resolut...

Climate changes will bring average warmer temperatures, elevated atmospheric CO2 and more frequent extreme weather events. These are expected to result in impacts on crop growth and cropping systems. However, traditional field and controlled environment experiments are limited in studying climate change impacts on crops due to the very large number...

Droughts continue to affect ecosystems, communities, and entire economies. Agriculture bears much of the impact, and in many countries it is the most heavily affected sector. Over the past decades, efforts have been made to assess drought risk at different spatial scales. Here, we present for the first time an integrated assessment of drought risk...

The potential impacts of climate warming on grain yield, water, and nitrogen consumptions of maize have been repeatedly assessed across different regions of the world. However, to date, there is no comprehensive, large-scale evaluation on the effects of climate warming on the cropping systems of Iran. The objective of the current study was to quant...

In times of drought, water resources are insufficient. These water shortages often have negative effects on agricultural productivity and on associated socioeconomic factors, causing reduced income, food shortages and even famines. The overall objective of GlobeDrought is to develop an integrated drought risk information system which will adequatel...

Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual...

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on g...

In order to study the possibility of using the chlorophyll fluorescence parameters for evaluation freezing tolerance of sugar beet varieties, an experiment was performed by using a factorial based on randomized complete block design with three replications at agricultural faculty of Ferdowsi University of Mashhad. Seven sugar beet varieties (Jolge,...

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multi model ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e‐mean) and median (e‐median) often seem to predict quite well. However few studies have specifically been concern...

The data set reported here includes the part of a Hot Serial Cereal Experiment (HSC) experiment recently used in the AgMIP-Wheat project to analyze the uncertainty of 30 wheat models and quantify their response to temperature. The HSC experiment was conducted in an open-field in a semiarid environment in the southwest USA. The data reported herewit...

Changing crop phenology is considered an important bio-indicator of climate change, with the recent warming trend causing an advancement in crop phenology. Little is known about the contributions of changes in sowing dates and cultivars to long-term trends in crop phenology, particularly for winter crops such as winter wheat. Here, we analyze a lon...

Previous studies suggested a wide range of sensitivities of wheat yields to heat stress around anthesis. The aim of this study was to improve the understanding of the reasons of the disagreement by testing the response of wheat yield and yield components to differences in the method of heating, the temperature measurement point and soil substrate u...

Despite widespread application in studying climate change impacts, most crop models ignore complex interactions among air temperature, crop and soil water status, CO2 concentration and atmospheric conditions that influence crop canopy temperature. The current study extended previous studies by evaluating Tc simulations from nine crop models at six...

Nendel 38 | Jørgen Eivind Olesen 37 | Taru Palosuo 44 | John R. Porter 42,45,46 | Eckart Priesack 39 | Dominique Ripoche 47 | Mikhail A. Semenov 48 | Claudio Stöckle 17 | Pierre Stratonovitch 48 | Thilo Streck 33 | Iwan Supit 49 | Fulu Tao 50,44

Nature Plants 3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical funct...

Change in cropping practices is required to address the food security issues in Africa. Yet, testing of the performance of such changes, in particular at large scales, often needs significant investments. Crop models are widely used tools to quantify the effects of agronomic decisions on cropping systems and to identify the most promising areas for...

Provision of food security in the face of increasing global food demand requires narrowing of the gap between actual farmer's yield and maximum attainable yield. So far, assessments of yield gaps have focused on average yield over 5-10. years, but yield gaps can vary substantially between crop seasons. In this study we hypothesized that climate-ind...

Martre P, Reynolds MP, Asseng S, Ewert F, Alderman PD, Cammarano D, Maiorano A, Ruane AC, Aggarwal PK, Anothai J, Basso B, Biernath C, Challinor AJ, De Sanctis G, Doltra J, Dumont B, Fereres E, Garcia-Vila M, Gayler S, Hoogenboom G, Hunt LA, Izaurralde RC, Jabloun M, Jones CD, Kassie BT, Kersebaum KC, Koehler AK, Müller C, Kumar SN, Liu B, Lobell D...

To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models...

The production of cereal crops is increasingly influenced by heat and drought stress. Despite the typical small-scale sub-regional variability of these stresses, impacts on yields are also of concern at larger regional to global scales. Crop growth models are the most widely used tools for simulating the effects of heat and drought stress on crop y...

Research on the impact of climate change on agricultural production has mainly focused on the effect of climate and its variability on individual crops, while the potential for adapting to climate change through crop substitution has received less attention. This is surprising because the proportions of individual crops in the total crop area have...

A spatial resolution needs to be determined prior to using models to simulate crop yields at a regional scale, but a dilemma exists in compromising between different demands. A fine spatial resolution demands extensive computation load for input data assembly, model runs, and output analysis. A coarse spatial resolution could result in loss of spat...

Performance of crop models at large scales is relatively uncertain since most of them were developed, parameterized and tested at field scale (Hansen and Jones, 2000). In addition, the spatial heterogeneity of study regions is generally not adequately considered at large scales (Ewert et al., 2011). Input variables mainly used for simulation of cro...

Globally, higher temperatures due to climate change are expected to increase crop consumptive water use, potentially leading to more water stress situations. Further, without adequate water supply, dry soils can cause heat stress by raising canopy temperatures as plants close their stomata and evapotranspiration (ET) is reduced. Dynamic, proces...

Variation of chlorophyll fluorescence parameters is an important criterion for selection of tolerant crop cultivars against stressful conditions such as freezing stress. In order to study the possibility of using the chlorophyll fluorescence parameters to evaluate freezing tolerance of fall sugar beet cultivars, an experiment was conducted by using...