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Journal articles on the topic 'Process-based crop models'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Wimalasiri, Eranga M., Sachini Ariyachandra, Aruna Jayawardhana, et al. "Process-Based Crop Models in Soil Research: A Bibliometric Analysis." Soil Systems 7, no. 2 (2023): 43. http://dx.doi.org/10.3390/soilsystems7020043.

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Different types of soil data are used in process-based crop models as input data. Crop models have a diverse range of applications, and soil research is one of them. This bibliographic analysis was conducted to assess the current literature on soil-related applications of crop models using two widely used crop models: Agricultural Production Systems Simulator (APSIM) and Decision Support System for Agrotechnology Transfer (DSSAT). The publications available in the Scopus database during the 2000–2021 period were assessed. Using 523 publications, a database on the application of process-based c

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2

Robertson, Richard, Gerald Nelson, Timothy Thomas, and Mark Rosegrant. "Incorporating Process‐Based Crop Simulation Models into Global Economic Analyses." American Journal of Agricultural Economics 95, no. 2 (2012): 228–35. http://dx.doi.org/10.1093/ajae/aas034.

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3

He, Di, Enli Wang, Jing Wang, and Michael J. Robertson. "Data requirement for effective calibration of process-based crop models." Agricultural and Forest Meteorology 234-235 (March 2017): 136–48. http://dx.doi.org/10.1016/j.agrformet.2016.12.015.

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Wimalasiri, Eranga M., Ebrahim Jahanshiri, Tengku Adhwa Syaherah Tengku Mohd Suhairi, et al. "Basic Soil Data Requirements for Process-Based Crop Models as a Basis for Crop Diversification." Sustainability 12, no. 18 (2020): 7781. http://dx.doi.org/10.3390/su12187781.

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Data from global soil databases are increasingly used for crop modelling, but the impact of such data on simulated crop yield has not been not extensively studied. Accurate yield estimation is particularly useful for yield mapping and crop diversification planning. In this article, available soil profile data across Sri Lanka were harmonised and compared with the data from two global soil databases (Soilgrids and Openlandmap). Their impact on simulated crop (rice) yield was studied using a pre-calibrated Agricultural Production Systems Simulator (APSIM) as an exemplar model. To identify the mo

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Wang, Enli, Hamish E. Brown, Greg J. Rebetzke, Zhigan Zhao, Bangyou Zheng, and Scott C. Chapman. "Improving process-based crop models to better capture genotype×environment×management interactions." Journal of Experimental Botany 70, no. 9 (2019): 2389–401. http://dx.doi.org/10.1093/jxb/erz092.

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Mattila, Tuomas J., Andrei Girz, and Noora Vihanto. "Identifying key parameters for cover crop C sequestration across process-based models." European Journal of Agronomy 170 (September 2025): 127771. https://doi.org/10.1016/j.eja.2025.127771.

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Maestrini, Bernardo, Gordan Mimić, Oort Pepijn van, et al. "Mixing process-based and data-driven approaches in yield prediction." European Journal of Agronomy 139 (July 8, 2022): 126569. https://doi.org/10.1016/j.eja.2022.126569.

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Yield prediction models can be divided between data-driven and process-based models (crop growth models). The first category contains many different types of models with parameters learned from the data themselves and where domain knowledge is only used to select the predictors and engineer features. In the second category, models are based upon biophysical principles, whose structure and parameters are derived primarily from domain knowledge. Here we investigate if the integration of the two approaches can be beneficial as it allows to overcome the limitations of the two approaches taken indi

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Roberts, Michael J., Noah O. Braun, Thomas R. Sinclair, David B. Lobell, and Wolfram Schlenker. "Comparing and combining process-based crop models and statistical models with some implications for climate change." Environmental Research Letters 12, no. 9 (2017): 095010. http://dx.doi.org/10.1088/1748-9326/aa7f33.

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9

Hernández-Ochoa, I. M. "Exploring climate change impacts and adaptation strategies in crop production by using process-based crop simulation models." Acta Horticulturae, no. 1425 (March 2025): 411–20. https://doi.org/10.17660/actahortic.2025.1425.53.

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Lobell, David B., and Senthold Asseng. "Comparing estimates of climate change impacts from process-based and statistical crop models." Environmental Research Letters 12, no. 1 (2017): 015001. http://dx.doi.org/10.1088/1748-9326/aa518a.

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11

Thorp, K. R., S. Ale, M. P. Bange, et al. "Development and Application of Process-based Simulation Models for Cotton Production: A Review of Past, Present, and Future Directions." Journal of Cotton Science 18, no. 1 (2014): 10–47. http://dx.doi.org/10.56454/oovc6073.

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The development and application of cropping system simulation models for cotton production has a long and rich history, beginning in the southeastern U. S. in the 1960s and now expanded to major cotton production regions globally. This paper briefly reviews the history of cotton simulation models, examines applications of the models since the turn of the century, and identifies opportunities for improving models and their use in cotton research and decision support. Cotton models reviewed include those specific to cotton (GOSSYM, Cotton2K, COTCO2, OZCOT, and CROPGRO-Cotton) and generic crop mo

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Rawat, Meenakshi, Vaishali Sharda, Xiaomao Lin, and Kraig Roozeboom. "Climate Change Impacts on Rainfed Maize Yields in Kansas: Statistical vs. Process-Based Models." Agronomy 13, no. 10 (2023): 2571. http://dx.doi.org/10.3390/agronomy13102571.

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The changing climate and the projected increase in the variability and frequency of extreme events make accurate predictions of crop yield critically important for addressing emerging challenges to food security. Accurate and timely crop yield predictions offer invaluable insights to agronomists, producers, and decision-makers. Even without considering climate change, several factors including the environment, management, genetics, and their complex interactions make such predictions formidably challenging. This study introduced a statistical-based multiple linear regression (MLR) model for th

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CÁRDENAS-POLONIO, Francisco, Javier MARTÍNEZ-DALMAU, and Julio BERBEL-VECINO. "Pistachio nut diffusion in Spain: Growth models." Spanish Journal of Agricultural Research 21, no. 1 (2023): e0103. http://dx.doi.org/10.5424/sjar/2023211-19474.

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Aim of study: To analyse the diffusion of the crop by producing forecast models, that intend to help farmers in their decision-making. Area of study: Spain. The area dedicated to pistachio cultivation in Spain has multiplied by 36 in the period 2010 to 2020, reaching 44,244 ha. Material and methods: This study brings together data on the evolution of pistachio cultivation based on the following parameters: cultivated area, yield, and price. Methods are based on internal, external influence models and on an influence-price-crop yield pattern. Main results: The results indicate

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14

Yin, Xiaogang, Kurt Christian Kersebaum, Chris Kollas, et al. "Performance of process-based models for simulation of grain N in crop rotations across Europe." Agricultural Systems 154 (June 2017): 63–77. http://dx.doi.org/10.1016/j.agsy.2017.03.005.

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15

Jame, Y. W., and H. W. Cutforth. "Crop growth models for decision support systems." Canadian Journal of Plant Science 76, no. 1 (1996): 9–19. http://dx.doi.org/10.4141/cjps96-003.

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Studies on crop production are traditionally carried out by using conventional experience-based agronomic research, in which crop production functions were derived from statistical analysis without referring to the underlying biological or physical principles involved. The weaknesses and disadvantages of this approach and the need for greater in-depth analysis have long been recognized. Recently, application of the knowledge-based systems approach to agricultural management has been gaining popularity because of our expanding knowledge of processes that are involved in the growth of plants, co

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Pysarenko, Nadiia, Elena Ablova, Alexander Dudko, Victor Malyarevsky, and Miroslav Kosyak. "Current models in the field of agricultural crops insurance." Problems of Innovation and Investment Development, no. 25 (June 30, 2021): 127–35. http://dx.doi.org/10.33813/2224-1213.25.2021.13.

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Agricultural production is an important and at the same time the most risky type of economic activity. Its reproductive process is associated with natural and climatic, biological and financial factors, the action of which in many cases is difficult to forgive and control. Crop production is particularly affected by cumulative natural risks. One of the ways to minimize agricultural risks is to use crop insurance as an important means of ensuring the riskiness of agricultural production from probable natural and weather factors. The pricing mechanism for crop insurance services is substantiated

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Beegum, Sahila, Charles Hunt Walne, Krishna N. Reddy, Vangimalla Reddy, and Kambham Raja Reddy. "Examining the Corn Seedling Emergence–Temperature Relationship for Recent Hybrids: Insights from Experimental Studies." Plants 12, no. 21 (2023): 3699. http://dx.doi.org/10.3390/plants12213699.

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Corn seedling emergence is a critical factor affecting crop yields. Accurately predicting emergence is crucial for precise crop growth and development simulation in process-based crop models. While various experimental studies have investigated the relationship between corn seedling emergence and temperature, there remains a scarcity of studies focused on newer corn hybrids. In the present study, statistical models (linear and quadratic functional relationships) are developed based on the seedling emergence of ten current corn hybrids, considering soil and air temperatures as influencing facto

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Yin, Xiaomeng, Guoyong Leng, and Linfei Yu. "Disentangling the separate and confounding effects of temperature and precipitation on global maize yield using machine learning, statistical and process crop models." Environmental Research Letters 17, no. 4 (2022): 044036. http://dx.doi.org/10.1088/1748-9326/ac5716.

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Abstract Temperature impacts on crop yield are known to be dependent on concurrent precipitation conditions and vice versa. To date, their confounding effects, as well as the associated uncertainties, are not well quantified at the global scale. Here, we disentangle the separate and confounding effects of temperature and precipitation on global maize yield under 25 climate scenarios. Instead of relying on a single type of crop model, as pursued in most previous impact assessments, we utilize machine learning, statistical and process-based crop models in a novel approach that allows for reasona

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Katzin, David, Eldert J. van Henten, and Simon van Mourik. "Process-based greenhouse climate models: Genealogy, current status, and future directions." Agricultural Systems 198 (April 2022): 103388. http://dx.doi.org/10.1016/j.agsy.2022.103388.

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Huber, Isaiah, Lizhi Wang, Jerry L. Hatfield, H. Mark Hanna, and Sotirios V. Archontoulis. "Modeling days suitable for fieldwork using machine learning, process-based, and rule-based models." Agricultural Systems 206 (March 2023): 103603. http://dx.doi.org/10.1016/j.agsy.2023.103603.

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21

Persson, T., M. Höglind, M. Van Oijen, et al. "Simulation of timothy nutritive value: A comparison of three process-based models." Field Crops Research 231 (February 2019): 81–92. http://dx.doi.org/10.1016/j.fcr.2018.11.008.

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22

Gunarathna, M. H. J. P., Kazuhito Sakai, M. K. N. Kumari, and Manjula Ranagalage. "A Functional Analysis of Pedotransfer Functions Developed for Sri Lankan soils: Applicability for Process-Based Crop Models." Agronomy 10, no. 2 (2020): 285. http://dx.doi.org/10.3390/agronomy10020285.

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As measurements are expensive and laborious, the estimation of soil hydraulic properties using pedotransfer functions (PTFs) has become popular worldwide. However, the estimation of soil hydraulic properties is not the final aim but an essential input value for other calculations and simulations, mostly in environmental and crop models. This modeling approach is a popular way to assess agricultural and environmental processes. However, it is rarely used in Sri Lanka because soil hydraulic data are rare. We evaluated the functionality of PTFs (developed to estimate field capacity (FC) and the p

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23

Iniyan, Shanmugam, Rethnaraj Jebakumar, and Mani Gayathri. "Selection of crop varieties and yield prediction based on phenotype applying deep learning." International Journal of Electrical and Computer Engineering (IJECE) 13 (December 1, 2023): 6806–16. https://doi.org/10.11591/ijece.v13i6.pp6806-6816.

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In India, agriculture plays an important role in the nation’s gross domestic product (GDP) and is also a part of civilization. Countries’ economies are also influenced by the amount of crop production. All business trading involves farming as a major factor. In order to increase crop production, different technological advancements are developed to acquire the information required for crop production. The proposed work is mainly focused on suitable crop selection across districts in Tamil Nadu, considering phenotype factors such as soil type, climatic factors, cropping se

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24

Guarin, Jose Rafael, Jonas Jägermeyr, Elizabeth A. Ainsworth, et al. "Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0." Geoscientific Model Development 17, no. 7 (2024): 2547–67. http://dx.doi.org/10.5194/gmd-17-2547-2024.

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Abstract. Elevated surface ozone (O3) concentrations can negatively impact growth and development of crop production by reducing photosynthesis and accelerating leaf senescence. Under unabated climate change, future global O3 concentrations are expected to increase in many regions, adding additional challenges to global agricultural production. Presently, few global process-based crop models consider the effects of O3 stress on crop growth. Here, we incorporated the effects of O3 stress on photosynthesis and leaf senescence into the Decision Support System for Agrotechnology Transfer (DSSAT) c

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25

Borrmann, Peter, Patric Brandt, and Heike Gerighausen. "MISPEL: A Multi-Crop Spectral Library for Statistical Crop Trait Retrieval and Agricultural Monitoring." Remote Sensing 15, no. 14 (2023): 3664. http://dx.doi.org/10.3390/rs15143664.

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Spatiotemporally accurate estimates of crop traits are essential for both scientific modeling and practical decision making in sustainable agricultural management. Besides efficient and concise methods to derive these traits, site- and crop-specific reference data are needed to develop and validate retrieval methods. To address this shortcoming, this study first includes the establishment of ’MISPEL’, a comprehensive spectral library (SpecLib) containing hyperspectral measurements and reference data for six key traits of ten widely grown crops. Secondly, crop-specific statistical leaf area ind

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26

Ringeval, Bruno, Christoph Müller, Thomas A. M. Pugh, et al. "Potential yield simulated by global gridded crop models: using a process-based emulator to explain their differences." Geoscientific Model Development 14, no. 3 (2021): 1639–56. http://dx.doi.org/10.5194/gmd-14-1639-2021.

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Abstract. How global gridded crop models (GGCMs) differ in their simulation of potential yield and reasons for those differences have never been assessed. The GGCM Intercomparison (GGCMI) offers a good framework for this assessment. Here, we built an emulator (called SMM for simple mechanistic model) of GGCMs based on generic and simplified formalism. The SMM equations describe crop phenology by a sum of growing degree days, canopy radiation absorption by the Beer–Lambert law, and its conversion into aboveground biomass by a radiation use efficiency (RUE). We fitted the parameters of this emul

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Zhang, Meng, Yanmei Gao, Yinghua Zhang, et al. "The contribution of spike photosynthesis to wheat yield needs to be considered in process-based crop models." Field Crops Research 257 (October 2020): 107931. http://dx.doi.org/10.1016/j.fcr.2020.107931.

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28

Caldararu, Silvia, Drew W. Purves, and Matthew J. Smith. "The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)." Geoscientific Model Development 10, no. 4 (2017): 1679–701. http://dx.doi.org/10.5194/gmd-10-1679-2017.

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Abstract. Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process-based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In this paper, we present a generic process-based crop model (PeakN-crop v1.0) which we parametrise using a Bayesian model-fitting algorithm to three different sources: data–space-based vegetation indices, eddy c

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Saddique, Qaisar, Yufeng Zou, Ali Ajaz, et al. "Analyzing the Performance and Application of CERES-Wheat and APSIM in the Guanzhong Plain, China." Transactions of the ASABE 63, no. 6 (2020): 1879–93. http://dx.doi.org/10.13031/trans.13631.

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HighlightsAPSIM and CERES-Wheat were calibrated and evaluated for winter wheat in the Guanzhong Plain, China.Both models performed well as the result of the calibration based on comprehensive field data.CERES-Wheat showed high sensitivity to field capacity.APSIM showed high sensitivity to nitrate at sowing.A crop model ensemble is better than a single model application, particularly for grain yield.Abstract. Cropping system models are useful tools to estimate the impact of climate and environment on agricultural production and to improve the management of agricultural systems. Numerous crop mo

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Afshar, Mehdi H., Timothy Foster, Thomas P. Higginbottom, et al. "Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring." Remote Sensing 13, no. 5 (2021): 924. http://dx.doi.org/10.3390/rs13050924.

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Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyse to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (Agricultural Production System sIMulator (APSIM)) applied for rice producers in Odisha, India, we simulat

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Kropff, M. J., and L. A. P. Lotz. "Optimization of Weed Management Systems: The Role of Ecological Models of Interplant Competition." Weed Technology 6, no. 2 (1992): 462–70. http://dx.doi.org/10.1017/s0890037x00035065.

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The strategy to optimize weed management systems with a minimum use of herbicides includes both the adaptation of crop management practices and well designed decision making systems, based on postemergence observations of weed infestations. Both strategies require thorough quantitative insight into the crop weed ecosystem, which can be provided by systems analysis, using process based models. These models also can be applied to similar systems like intercropping. For practical application, however, a simple measure of weed infestation and a simple model which relates weed infestation to yield

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Smith, W. N., B. B. Grant, R. L. Desjardins, P. Rochette, C. F. Drury, and C. Li. "Evaluation of two process-based models to estimate soil N2O emissions in Eastern Canada." Canadian Journal of Soil Science 88, no. 2 (2008): 251–60. http://dx.doi.org/10.4141/cjss06030.

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Process-based models play an important role in the estimation of soil N2O emissions from regions with contrasting soil and climatic conditions. A study was performed to evaluate the ability of two process-based models, DAYCENT and DNDC, to estimate N2O emissions, soil nitrate- and ammonium-N levels, as well as soil temperature and water content. The measurement sites included a maize crop fertilized with pig slurry (Quebec) and a wheat-maize-soybean rotation as part of a tillage-fertilizer experiment (Ontario). At the Quebec site, both models accurately simulated soil temperature with an avera

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McKinion, James M. "MECHANICS OF MODEL BUILDING." HortScience 28, no. 5 (1993): 513d—513. http://dx.doi.org/10.21273/hortsci.28.5.513d.

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In the late 1960's with the advent of wide availability of the digital computer, it became possible through the application of systems theory to build simulation models of biological entities. In production agriculture, scientists have built models of a number of field crops: cotton, corn, wheat, soybean, and alfalfa. In most of the models built to date, crop phenology is addressed directly with detailed aspects of crop physiology and/or soil physics addressed either to a greater or lessor degree. This paper addresses the methodology of the construction of process-level, physiologically-based

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Benaly, Mohamed Amine, Youssef Brouziyne, Lhoussaine Bouchaou, Mohamed Hakim Kharrou, and Abdelghani Chehbouni. "Review of crop modelling approaches to address climate change challenges in Africa." E3S Web of Conferences 492 (2024): 04001. http://dx.doi.org/10.1051/e3sconf/202449204001.

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Africa is facing an urgent need to increase food production to meet increasing demands. Targeted investments in integrated agriculture and, water management systems are required to meet this challenge. However, there is a lack of comprehensive information on the potential applications of climate-smart agriculture (CSA). This paper reviews current crop modeling technologies and their applications within the scope of climate change and the CSA framework in Africa. It evaluates current research trends in various crop simulation models and suggest advanced approaches to improve crop and environmen

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Tolomio, Massimo, and Raffaele Casa. "Dynamic Crop Models and Remote Sensing Irrigation Decision Support Systems: A Review of Water Stress Concepts for Improved Estimation of Water Requirements." Remote Sensing 12, no. 23 (2020): 3945. http://dx.doi.org/10.3390/rs12233945.

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Novel technologies for estimating crop water needs include mainly remote sensing evapotranspiration estimates and decision support systems (DSS) for irrigation scheduling. This work provides several examples of these approaches, that have been adjusted and modified over the years to provide a better representation of the soil–plant–atmosphere continuum and overcome their limitations. Dynamic crop simulation models synthetize in a formal way the relevant knowledge on the causal relationships between agroecosystem components. Among these, plant–water–soil relationships, water stress and its effe

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V, Shruthi. "Machine Learning Based Weed Crop Classification Using Raspberry PI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49185.

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CHAPTER 1 Introduction Weed-crop classification using machine learning is an emerging area of research in precision agriculture that aims to automate the process of identifying and distinguishing weeds from crops in agricultural fields. Weeds compete with crops for essential resources such as sunlight, water, and nutrients, ultimately reducing crop yield and quality. Traditional weed control methods, including manual weeding and blanket application of herbicides, are often labor-intensive, time-consuming, and environmentally harmful. To address these challenges, machine learning techniques, pa

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Patel Dineshkumar Vinubhai, Kamalesh V. N, and Madhukar G. "CROP IMAGE CLASSIFICATION." Scientific Digest : Journal of Applied Engineering 13, no. 7(1) (2025): 92–101. https://doi.org/10.70864/joae.2025.v13.i7(1).pp92-101.

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Emotion detection plays a vital role in advancing human-computer interaction by enabling systems to recognize and respond appropriately to human emotions. This study introduces a deep learning-based multimodal emotion detection model that combines speech recognition and facial expression analysis to enhance classification accuracy. The proposed approach utilizes Convolutional Neural Network (CNN) architectures to simultaneously process audio signals and facial images, effectively capturing complementary information from both data types. While traditional methods like Random Forest Classifier (

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Ma, D., Q. Jing, YP Xu, et al. "Using ensemble-mean climate scenarios for future crop yield projections: a stochastic weather generator approach." Climate Research 83 (May 6, 2021): 161–71. http://dx.doi.org/10.3354/cr01646.

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Using climate scenarios from only 1 or a small number of global climate models (GCMs) in climate change impact studies may lead to biased assessment due to large uncertainty in climate projections. Ensemble means in impact projections derived from a multi-GCM ensemble are often used as best estimates to reduce bias. However, it is often time consuming to run process-based models (e.g. hydrological and crop models) in climate change impact studies using numerous climate scenarios. It would be interesting to investigate if using a reduced number of climate scenarios could lead to a reasonable es

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Ekanayake, Piyal, Lasini Wickramasinghe, and Jeevani W. Jayasinghe. "Development of Crop-Weather Models Using Gaussian Process Regression for the Prediction of Paddy Yield in Sri Lanka." International Journal of Intelligent Systems and Applications 14, no. 4 (2022): 52–665. http://dx.doi.org/10.5815/ijisa.2022.04.05.

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This research introduces machine learning models using the Gaussian Process Regression (GPR) depicting the association between paddy yield and weather in Sri Lanka. All major regions in the island with most contribution to the total paddy production were considered in this research. The climatic factors of rainfall, relative humidity, minimum temperature, maximum temperature, average wind speed, evaporation, and sunshine hours were considered as input (independent) variables, while the paddy yield was the output (dependent) variable. The collinearity within each pair of independent and depende

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Jong, R. de, and A. Bootsma. "Review of recent developments in soil water simulation models." Canadian Journal of Soil Science 76, no. 3 (1996): 263–73. http://dx.doi.org/10.4141/cjss96-033.

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Soil water is of great importance to agricultural and hydrological systems, affecting crop yields, agricultural management practices and a wide range of physical and chemical processes in soils. Many models have been developed over the years to simulate soil water, ranging from simple water balance procedures to complex deterministic models. In this paper, some of the basic concepts, strengths, weaknesses and input requirements of soil water models are reviewed. Simple budget models which require only available water-holding capacity, based on the concepts of field capacity and wilting point,

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Asse, Daphné, Christophe F. Randin, Marc Bonhomme, Anne Delestrade, and Isabelle Chuine. "Process-based models outcompete correlative models in projecting spring phenology of trees in a future warmer climate." Agricultural and Forest Meteorology 285-286 (May 2020): 107931. http://dx.doi.org/10.1016/j.agrformet.2020.107931.

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Franke, James A., Christoph Müller, Joshua Elliott, et al. "The GGCMI Phase 2 emulators: global gridded crop model responses to changes in CO<sub>2</sub>, temperature, water, and nitrogen (version 1.0)." Geoscientific Model Development 13, no. 9 (2020): 3995–4018. http://dx.doi.org/10.5194/gmd-13-3995-2020.

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Abstract. Statistical emulation allows combining advantageous features of statistical and process-based crop models for understanding the effects of future climate changes on crop yields. We describe here the development of emulators for nine process-based crop models and five crops using output from the Global Gridded Model Intercomparison Project (GGCMI) Phase 2. The GGCMI Phase 2 experiment is designed with the explicit goal of producing a structured training dataset for emulator development that samples across four dimensions relevant to crop yields: atmospheric carbon dioxide (CO2) concen

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Dananjali, K. T., J. B. Ekanayake, B. T. G. S. Kumara, and A. S. Karunarathne. "Crop Prediction Based on Environment Variables using Data Mining Technologies." Journal of Agricultural Sciences – Sri Lanka 20, no. 2 (2025): 280–92. https://doi.org/10.4038/jas.v20i2.9901.

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Purpose: Sustainable agriculture is essential for addressing food security challenges and enhancing the socio-economic status of farmers. Integrating modern technologies with the agricultural sector is a key solution to overcome many issues. Therefore, this study aimed to apply data mining technologies to identify the most suitable crop types for specific land based on factors: weather conditions (rainfall, temperature, humidity), crop prices, and soil conditions of lands. Potato, tomato, green gram, and red onions are crop types. The soil conditions are determined based on the locations speci

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Garg, Rachit, and Brahma Reddy. "Crop Recommendation System." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–9. https://doi.org/10.55041/isjem02794.

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Automating agricultural aspects is a mechanical process with or without human intervention in agriculture. Due to less space of domestic lands, it has become an important area of choosing the most suitable crops based on prevailing factors in the selected area. In Sri Lankan even though there are enough knowledge, techniques, and methods which are done manually available in agriculture, there is not any system in which the environmental factors are detected and suggests the user which crop type is best for farming. This paper is consisting of a theoretical and conceptual platform of Recommenda

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Guo, Tian, Bernard A. Engel, Gang Shao, Jeffrey G. Arnold, Raghavan Srinivasan, and James R. Kiniry. "Functional Approach to Simulating Short-Rotation Woody Crops in Process-Based Models." BioEnergy Research 8, no. 4 (2015): 1598–613. http://dx.doi.org/10.1007/s12155-015-9615-0.

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Liu, Weihang, Zitong Li, Yan Li, Tao Ye, Shuo Chen, and Yiqing Liu. "Heterogeneous impacts of excessive wetness on maize yields in China: Evidence from statistical yields and process-based crop models." Agricultural and Forest Meteorology 327 (December 2022): 109205. http://dx.doi.org/10.1016/j.agrformet.2022.109205.

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Leng, Guoyong, and Jim W. Hall. "Predicting spatial and temporal variability in crop yields: an inter-comparison of machine learning, regression and process-based models." Environmental Research Letters 15, no. 4 (2020): 044027. http://dx.doi.org/10.1088/1748-9326/ab7b24.

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Pohanková, E., P. Hlavinka, M. Orság, et al. "Estimating the water use efficiency of spring barley using crop models." Journal of Agricultural Science 156, no. 5 (2018): 628–44. http://dx.doi.org/10.1017/s0021859618000060.

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AbstractIn the current study, simulations by five crop models (WOFOST, CERES-Barley, HERMES, DAISY and AQUACROP) were compared for 7–12 growing seasons of spring barley (Hordeum vulgare) at three sites in the Czech Republic. The aims were to compare how various process-based crop models with different calculation approaches simulate different values of transpiration (Ta) and evapotranspiration (ET) based on the same input data and compare the outputs of these simulations with reference data. From the outputs of each model, the water use efficiency (WUE) from Ta (WUETa) and from actual ET (WUEE

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Wallor, Evelyn, Kurt-Christian Kersebaum, Domenico Ventrella, et al. "The response of process-based agro-ecosystem models to within-field variability in site conditions." Field Crops Research 228 (November 2018): 1–19. http://dx.doi.org/10.1016/j.fcr.2018.08.021.

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Williams, A. S., D. M. Mushet, M. Lang, et al. "Improving the ability to include freshwater wetland plants in process-based models." Journal of Soil and Water Conservation 75, no. 6 (2020): 704–12. http://dx.doi.org/10.2489/jswc.2020.00089.

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