Predictions and Estimations in Agricultural Production under a Changing Climate

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Agricultural Biosystem and Biological Engineering".

Deadline for manuscript submissions: closed (20 September 2023) | Viewed by 28976

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Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: artificial neural networks; artificial intelligence; machine learning; yield modelling; predictions; forecasting; crop production
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Department of Geoecology and Geoinformation, Institute of Biology and Earth Sciences, Pomeranian University in Słupsk, 27 Partyzantów St., 76-200 Słupsk, Poland
Interests: artificial neural networks; artificial intelligence; machine learning; yield modelling; predictions; potato production; plant breeding; soil science; plant growth analysis
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Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: agricutural engineering; soil tillage; precison agriculture; soil monitoring, proximal sensing, spectroscopy; digital farming; smart farming
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Field and Horticultural Crops Research Department, Kurdistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Jam-e Jam Cross Way, P.O. Box 741, 66169-36311 Sanandaj, Iran
Interests: plant breeding; plant tissue culture; gene transformation; statistical designs; machine learning
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Special Issue Information

Dear Colleagues,

Prediction is the rational and scientific anticipation of future events in order to reduce risk in the decision-making process. Prediction in today’s agriculture is a very important aspect of improving and refining the management of any agricultural activity. Predictive analytics is increasingly being used in agriculture not only to describe large-scale processes, but also at the scale of individual crop fields. The results of such forecasts can help to decide on many current activities during the growing season: the date of harvesting, or plant protection treatments. Up-to-date forecasts make it possible to monitor the prepared storage area and to estimate the necessary inputs. Forecasting is becoming increasingly important under climate change. These inevitable changes have a huge impact on the transformation of ecosystems, both natural and under strict human control. Taking into account the above arguments, it is worth developing systems for reliable monitoring and prediction of multistage agricultural production, which will allow, among other things, estimating in advance the possible production effects to be achieved in both atypical years and standard conditions. Simulations of processes occurring in food production help to understand the combined effects of water and nutrient deficiencies, pests, diseases, the impact of yield variability, and other field conditions during the growing season. In other words, they integrate multiple factors affecting the final production outcome with relatively low prediction error. Currently, tools supporting prediction in agriculture include classical statistical models, machine learning, GIS tools, satellite and aerial remote sensing, the Internet of Things, or big data. The abovementioned techniques have become allies of decision makers in key decision-making processes, supporting industry databases with relevant information necessary in the process of managing and monitoring agricultural production.

Prof. Dr. Gniewko Niedbała
Dr. Magdalena Piekutowska
Dr. Tomasz Wojciechowski
Dr. Mohsen Niazian
Guest Editor

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Keywords

  • yield prediction
  • predictive analytics
  • crop maturity prediction
  • crop quality and quantity prediction
  • machine learning
  • artificial neural networks
  • crop models and modeling
  • agrometeorological models
  • model application for sustainable agriculture
  • crop monitoring
  • proximal and remote sensing for agriculture
  • IoT and big data
  • data science
  • predictive agriculture
  • precision agriculture
  • smart farming

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Published Papers (11 papers)

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Editorial

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4 pages, 184 KiB  
Editorial
Predictions and Estimations in Agricultural Production under a Changing Climate
by Gniewko Niedbała, Magdalena Piekutowska, Tomasz Wojciechowski and Mohsen Niazian
Agronomy 2024, 14(2), 253; https://doi.org/10.3390/agronomy14020253 - 24 Jan 2024
Viewed by 776
Abstract
In the 21st century, agriculture is facing numerous challenges [...] Full article

Research

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13 pages, 1722 KiB  
Article
Prediction of Grain Yield in Wheat by CHAID and MARS Algorithms Analyses
by Fatih Demirel, Baris Eren, Abdurrahim Yilmaz, Aras Türkoğlu, Kamil Haliloğlu, Gniewko Niedbała, Henryk Bujak, Bita Jamshidi, Alireza Pour-Aboughadareh, Jan Bocianowski and Kamila Nowosad
Agronomy 2023, 13(6), 1438; https://doi.org/10.3390/agronomy13061438 - 23 May 2023
Cited by 10 | Viewed by 1615
Abstract
Genetic information obtained from ancestral species of wheat and other registered wheat has brought about critical research, especially in wheat breeding, and shown great potential for the development of advanced breeding techniques. The purpose of this study was to determine correlations between some [...] Read more.
Genetic information obtained from ancestral species of wheat and other registered wheat has brought about critical research, especially in wheat breeding, and shown great potential for the development of advanced breeding techniques. The purpose of this study was to determine correlations between some morphological traits of various wheat (Triticum spp.) species and to demonstrate the application of MARS and CHAID algorithms to wheat-derived data sets. Relationships among several morphological traits of wheat were investigated using a total of 26 different wheat genotypes. MARS and CHAID data mining methods were compared for grain yield prediction from different traits using cross-validation. In addition, an optimal CHAID tree structure with minimum RMSE was obtained and cross-validated with nine terminal nodes. Based on the smallest RMSE of the cross-validation, the eight-element MARS model was found to be the best model for grain yield prediction. The MARS algorithm proved superior to CHAID in grain yield prediction and accounted for 95.7% of the variation in grain yield among wheats. CHAID and MARS analyses on wheat grain yield were performed for the first time in this research. In this context, we showed how MARS and CHAID algorithms can help wheat breeders describe complex interaction effects more precisely. With the data mining methodology demonstrated in this study, breeders can predict which wheat traits are beneficial for increasing grain yield. The adaption of MARS and CHAID algorithms should benefit breeding research. Full article
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21 pages, 5180 KiB  
Article
Development and Validation of Innovative Machine Learning Models for Predicting Date Palm Mite Infestation on Fruits
by Maged Mohammed, Hamadttu El-Shafie and Muhammad Munir
Agronomy 2023, 13(2), 494; https://doi.org/10.3390/agronomy13020494 - 8 Feb 2023
Cited by 9 | Viewed by 1920
Abstract
The date palm mite (DPM), Oligonychus afrasiaticus (McGregor), is a key pest of unripe date fruits. The detection of this mite depends largely on the visual observations of the webs it produces on the green fruits. One of the most important problems of [...] Read more.
The date palm mite (DPM), Oligonychus afrasiaticus (McGregor), is a key pest of unripe date fruits. The detection of this mite depends largely on the visual observations of the webs it produces on the green fruits. One of the most important problems of DPM control is the lack of an accurate decision-making approach for monitoring and predicting infestation on date fruits. Therefore, this study aimed to develop, evaluate, and validate prediction models for DPM infestation on fruits based on meteorological variables (temperature, relative humidity, wind speed, and solar radiation) and the physicochemical properties of date fruits (weight, firmness, moisture content, total soluble solids, total sugar, and tannin content) using two machine learning (ML) algorithms, i.e., linear regression (LR) and decision forest regression (DFR). The meteorological variables data in the study area were acquired using an IoT-based weather station. The physicochemical properties of two popular date palm cultivars, i.e., Khalas and Barhee, were analyzed at different fruit development stages. The development and performance of the LR and DFR prediction models were implemented using Microsoft Azure ML. The evaluation of the developed models indicated that the DFR was more accurate than the LR model in predicting the DPM based on the input variables, i.e., meteorological variables (R2 = 0.842), physicochemical properties variables (R2 = 0.895), and the combination of both meteorological and the physicochemical properties variables (R2 = 0.921). Accordingly, the developed DFR model was deployed as a fully functional prediction web service into the Azure cloud platform and the Excel add-ins. The validation of the deployed DFR model showed that it was able to predict the DPM count on date palm fruits based on the combination of meteorological and physicochemical properties variables (R2 = 0.918). The deployed DFR model by the web service of Azure Ml studio enhanced the prediction of the DPM count on the date fruits as a fast and easy-to-use approach. These findings demonstrated that the DFR model using Azure Ml Studio integrated into the Azure platform can be a powerful tool in integrated DPM management. Full article
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15 pages, 5093 KiB  
Article
Quantification and Evaluation of Water Requirements of Oil Palm Cultivation for Different Climate Change Scenarios in the Central Pacific of Costa Rica Using APSIM
by Fernando Watson-Hernández, Valeria Serrano-Núñez, Natalia Gómez-Calderón and Rouverson Pereira da Silva
Agronomy 2023, 13(1), 19; https://doi.org/10.3390/agronomy13010019 - 21 Dec 2022
Cited by 3 | Viewed by 2055
Abstract
Climate change is a variation in the normal behavior of the climate. These variations and their effects will be seen in the coming years, the most imminent being anomalous fluctuations in atmospheric temperature and precipitation. This scenario is counterproductive for agricultural production. This [...] Read more.
Climate change is a variation in the normal behavior of the climate. These variations and their effects will be seen in the coming years, the most imminent being anomalous fluctuations in atmospheric temperature and precipitation. This scenario is counterproductive for agricultural production. This study evaluated the effect of climate change on oil palm production for conditions in the Central Pacific of Costa Rica, in three simulation scenarios: the baseline between the years 2000 and 2019, a first climate change scenario from 2040 to 2059 (CCS1), and a second one from 2080 to 2099 (CCS2), using the modeling framework APSIM, and the necessary water requirements were established as an adaptive measure for the crop with the irrigation module. A decrease in annual precipitation of 5.55% and 7.86% and an increase in the average temperature of 1.73 °C and 3.31 °C were identified, generating a decrease in production yields of 7.86% and 37.86%, concerning the Baseline, in CCS1 and CCS2, respectively. Irrigation made it possible to adapt the available water conditions in the soil to maintain the baseline yields of the oil palm crop for the proposed climate change scenarios. Full article
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28 pages, 5678 KiB  
Article
Applying Spatial Statistical Analysis to Ordinal Data for Soybean Iron Deficiency Chlorosis
by Zhanyou Xu, Steven B. Cannon and William D. Beavis
Agronomy 2022, 12(9), 2095; https://doi.org/10.3390/agronomy12092095 - 1 Sep 2022
Cited by 2 | Viewed by 1857
Abstract
Accounting for field variation patterns plays a crucial role in interpreting phenotype data and, thus, in plant breeding. Several spatial models have been developed to account for field variation. Spatial analyses show that spatial models can successfully increase the quality of phenotype measurements [...] Read more.
Accounting for field variation patterns plays a crucial role in interpreting phenotype data and, thus, in plant breeding. Several spatial models have been developed to account for field variation. Spatial analyses show that spatial models can successfully increase the quality of phenotype measurements and subsequent selection accuracy for continuous data types such as grain yield and plant height. The phenotypic data for stress traits are usually recorded in ordinal data scores but are traditionally treated as numerical values with normal distribution, such as iron deficiency chlorosis (IDC). The effectiveness of spatial adjustment for ordinal data has not been systematically compared. The research objective described here is to evaluate methods for spatial adjustment of ordinal data, using soybean IDC as an example. Comparisons of adjustment effectiveness for spatial autocorrelation were conducted among eight different models. The models were divided into three groups: Group I, moving average grid adjustment; group II, geospatial autoregressive regression (SAR) models; and Group III, tensor product penalized P-splines. Results from the model comparison show that the effectiveness of the models depends on the severity of field variation, the irregularity of the variation pattern, and the model used. The geospatial SAR models outperform the other models for ordinal IDC data. Prediction accuracy for the lines planted in the IDC high-pressure area is 11.9% higher than those planted in low-IDC-pressure regions. The relative efficiency of the mixed SAR model is 175%, relative to the baseline ordinary least squares model. Even though the geospatial SAR model is the best among all the compared models, the efficiency is not as good for ordinal data types as for numeric data. Full article
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21 pages, 9050 KiB  
Article
Spatial Rice Yield Estimation Using Multiple Linear Regression Analysis, Semi-Physical Approach and Assimilating SAR Satellite Derived Products with DSSAT Crop Simulation Model
by Sellaperumal Pazhanivelan, Vellingiri Geethalakshmi, R. Tamilmounika, N. S. Sudarmanian, Ragunath Kaliaperumal, Kumaraperumal Ramalingam, A. P. Sivamurugan, Kancheti Mrunalini, Manoj Kumar Yadav and Emma D. Quicho
Agronomy 2022, 12(9), 2008; https://doi.org/10.3390/agronomy12092008 - 25 Aug 2022
Cited by 16 | Viewed by 3879
Abstract
Accurate and consistent information on the area and production of field crops is vital for national and state planning and ensuring food security in India. Satellite-based remote sensing offers a suitable and cost-effective technique for regional- and national-scale crop monitoring. The use of [...] Read more.
Accurate and consistent information on the area and production of field crops is vital for national and state planning and ensuring food security in India. Satellite-based remote sensing offers a suitable and cost-effective technique for regional- and national-scale crop monitoring. The use of remote sensing data for crop yield estimation has been demonstrated using a semi-physical approach with reasonable success. Assimilating remote sensing data with the DSSAT model and spectral indices-based regression analysis are promising methods for spatially estimating rice crop yields. Rice area and yield in the Cauvery delta zone of Tamil Nadu, India was estimated during samba (August–January) season in the years 2020–2021 using Sentinel 1A Synthetic Aperture Radar satellite data with three different spatial yield estimation methods, namely a spectral indices-based regression analysis, semi-physical approach, and integrating remote products with DSSAT crop growth model. A rice area map was generated for the study area using a rule-based classifier approach utilizing parameterization with a classification accuracy of 94.5% and a kappa score of 0.89. The total classified rice area in Cauvery Delta Region was 379,767 ha, and the Start of Season (SoS) maps for samba season revealed that the major planting period for rice was between 22 September and 9 November in 2020. The study also aimed to identify promising spatial yield estimation techniques for optimal rice yield prediction over large areas. Regression models resulted in rice yields of 3234 to 3905 kg ha−1 with a mean of 3654 kg ha−1. The net primary product was computed using the periodical PAR, fAPAR, Wstress, Tstress, and maximum radiation use efficiency in a semi-physical approach. The resultant rice yields ranged between 2652 and 3438 kg ha−1 with the mean of 3076 kg ha−1. During the integration of remote sensing products with crop growth models, LAI values were extracted from dB images and utilized to simulate rice yields in the range of 3684 to 4012 kg ha−1 with the mean of 3855 kg ha−1. When compared to the semi-physical approach, both integrating remote sensing products with the DSSAT crop growth model and spectral indices-based regression analysis had R2 greater than 0.80, NRMSE of less than 10%, and agreement of more than 90%, indicating that these two approaches could be used for spatial rice yield estimation. Full article
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23 pages, 2644 KiB  
Article
Application of Artificial Neural Networks Sensitivity Analysis for the Pre-Identification of Highly Significant Factors Influencing the Yield and Digestibility of Grassland Sward in the Climatic Conditions of Central Poland
by Gniewko Niedbała, Barbara Wróbel, Magdalena Piekutowska, Waldemar Zielewicz, Anna Paszkiewicz-Jasińska, Tomasz Wojciechowski and Mohsen Niazian
Agronomy 2022, 12(5), 1133; https://doi.org/10.3390/agronomy12051133 - 8 May 2022
Cited by 12 | Viewed by 2184
Abstract
Progressive climate changes are the most important challenges for modern agriculture. Permanent grassland represents around 70% of all agricultural land. In comparison with other agroecosystems, grasslands are more sensitive to climate change. The aim of this study was to create deterministic models based [...] Read more.
Progressive climate changes are the most important challenges for modern agriculture. Permanent grassland represents around 70% of all agricultural land. In comparison with other agroecosystems, grasslands are more sensitive to climate change. The aim of this study was to create deterministic models based on artificial neural networks to identify highly significant factors influencing the yield and digestibility of grassland sward in the climatic conditions of central Poland. The models were based on data from a grassland experiment conducted between 2014 and 2016. Phytophenological data (harvest date and botanical composition of sward) and meteorological data (average temperatures, total rainfall, and total effective temperatures) were used as independent variables, whereas qualitative and quantitative parameters of the feed made from the grassland sward (dry matter digestibility, dry matter yield, and protein yield) were used as dependent variables. Nine deterministic models were proposed Y_G, DIG_G, P_G, Y_GB, DIG_GB, P_GB, Y_GC, DIG_GC, and P_GC, which differed in the input variable and the main factor from the grassland experiment. The analysis of the sensitivity of the neural networks in the models enabled the identification of the independent variables with the greatest influence on the yield of dry matter and protein as well as the digestibility of the dry matter of the first regrowth of grassland sward, taking its diverse botanical composition into account. The results showed that the following factors were the most significant (rank 1): the average daily air temperature, total rainfall, and the percentage of legume plants. This research will be continued on a larger group of factors influencing the output variables and it will involve an attempt to optimise these factors. Full article
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13 pages, 2688 KiB  
Article
Best Linear Unbiased Predictions of Environmental Effects on Grain Yield in Maize Variety Trials of Different Maturity Groups
by Marina Zorić, Jerko Gunjača, Vlatko Galić, Goran Jukić, Ivan Varnica and Domagoj Šimić
Agronomy 2022, 12(4), 922; https://doi.org/10.3390/agronomy12040922 - 12 Apr 2022
Cited by 4 | Viewed by 1789
Abstract
Development of new cultivars and agronomic improvements are key factors of increasing in future grain yield in maize grown in environments affected by climate change. Assessment of value for cultivation and use (VCU) reflects the results of latest breeding efforts showing yield trends, [...] Read more.
Development of new cultivars and agronomic improvements are key factors of increasing in future grain yield in maize grown in environments affected by climate change. Assessment of value for cultivation and use (VCU) reflects the results of latest breeding efforts showing yield trends, whereby external environmental covariates were rarely used. This study aimed to analyze several environmental effects including stress degree days (SDD) on grain yields in Croatian VCU trials in three maturity groups using linear mixed model for the estimation of fixed and random effects. Best linear unbiased predictions (BLUPs) of location-year interaction showed no pattern among maturity groups. SDD showed mostly non-significant coefficients of regression on location BLUPs for yield. Analyzing location BLUPs, it was shown that the effect became consistently stronger with later maturity, either positive or negative. The effects of management might play more critical role in maize phenology and yield formation compared with climate change, at least in suboptimum growing conditions often found in Southeast Europe. To facilitate more robust predictions of the crop improvement, the traditional forked approach dealing with G × E by breeders and E × M by agronomists should be integrated to G × E × M framework, to assess the full gradient of combinations forming the adaptation landscape. Full article
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16 pages, 10749 KiB  
Article
Climate-Based Modeling and Prediction of Rice Gall Midge Populations Using Count Time Series and Machine Learning Approaches
by Santosha Rathod, Sridhar Yerram, Prawin Arya, Gururaj Katti, Jhansi Rani, Ayyagari Phani Padmakumari, Nethi Somasekhar, Chintalapati Padmavathi, Gabrijel Ondrasek, Srinivasan Amudan, Seetalam Malathi, Nalla Mallikarjuna Rao, Kolandhaivelu Karthikeyan, Nemichand Mandawi, Pitchiahpillai Muthuraman and Raman Meenakshi Sundaram
Agronomy 2022, 12(1), 22; https://doi.org/10.3390/agronomy12010022 - 23 Dec 2021
Cited by 13 | Viewed by 4347
Abstract
The Asian rice gall midge (Orseolia oryzae (Wood-Mason)) is a major insect pest in rice cultivation. Therefore, development of a reliable system for the timely prediction of this insect would be a valuable tool in pest management. In this study, occurring between [...] Read more.
The Asian rice gall midge (Orseolia oryzae (Wood-Mason)) is a major insect pest in rice cultivation. Therefore, development of a reliable system for the timely prediction of this insect would be a valuable tool in pest management. In this study, occurring between the period from 2013–2018: (i) gall midge populations were recorded using a light trap with an incandescent bulb, and (ii) climatological parameters (air temperature, air relative humidity, rainfall and insulations) were measured at four intensive rice cropping agroecosystems that are endemic for gall midge incidence in India. In addition, weekly cumulative trapped gall midge populations and weekly averages of climatological data were subjected to count time series (Integer-valued Generalized Autoregressive Conditional Heteroscedastic—INGARCH) and machine learning (Artificial Neural Network—ANN, and Support Vector Regression—SVR) models. The empirical results revealed that the ANN with exogenous variable (ANNX) model outperformed INGRACH with exogenous variable (INGRCHX) and SVR with exogenous variable (SVRX) models in the prediction of gall midge populations in both training and testing data sets. Moreover, the Diebold–Mariano (DM) test confirmed the significant superiority of the ANNX model over INGARCHX and SVRX models in modeling and predicting rice gall midge populations. Utilizing the presented efficient early warning system based on a robust statistical model to predict the build-up of gall midge population could greatly contribute to the design and implementation of both proactive and more sustainable site-specific pest management strategies to avoid significant rice yield losses. Full article
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15 pages, 1141 KiB  
Article
Two-Stage Spatiotemporal Time Series Modelling Approach for Rice Yield Prediction & Advanced Agroecosystem Management
by Santosha Rathod, Amit Saha, Rahul Patil, Gabrijel Ondrasek, Channappa Gireesh, Madhyavenkatapura Siddaiah Anantha, Dhumannatarao Venkata Krishna Nageswara Rao, Nirmala Bandumula, Ponnuvel Senguttuvel, Arun Kumar Swarnaraj, Shaik N. Meera, Amtul Waris, Ponnuraj Jeyakumar, Brajendra Parmar, Pitchiahpillai Muthuraman and Raman Meenakshi Sundaram
Agronomy 2021, 11(12), 2502; https://doi.org/10.3390/agronomy11122502 - 9 Dec 2021
Cited by 8 | Viewed by 3270
Abstract
A robust forecast of rice yields is of great importance for medium-to-long-term planning and decision-making in cereal production, from regional to national level. Incorporation of spatially correlated adjacent effects in forecasting models in general, results in accurate forecast. The Space Time Autoregressive Moving [...] Read more.
A robust forecast of rice yields is of great importance for medium-to-long-term planning and decision-making in cereal production, from regional to national level. Incorporation of spatially correlated adjacent effects in forecasting models in general, results in accurate forecast. The Space Time Autoregressive Moving Average (STARMA) is the most popular class of model in linear spatiotemporal time series modelling. However, STARMA cannot process nonlinear spatiotemporal relationships in datasets. Alternately, Time Delay Neural Network (TDNN) is a most popular machine learning algorithm to model the nonlinear pattern in data. To overcome these limitations, two-stage STARMA approach was developed to predict rice yield in some of the most intensive national rice agroecosystems in India. The Mean Absolute Percentage Errors value of proposed STARMA-II approach is lower compared to Autoregressive Moving Average (ARIMA) and STARMA model in all examined districts, while the Diebold-Mariano test confirmed that STARMA-II model is significantly different from classical approaches. The proposed STARMA-II approach is promising alternative to classical linear and nonlinear spatiotemporal time series models for estimating mixed linear and nonlinear patterns and can be advanced tool for mid-to-long-term sustainable planning and management of crop yields and patterns in agroecosystems, i.e., food supply and demand from local to regional levels. Full article
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14 pages, 1237 KiB  
Article
Assessment and Prediction of Maize Production Considering Climate Change by Extreme Learning Machine in Czechia
by Mansoor Maitah, Karel Malec, Ying Ge, Zdeňka Gebeltová, Luboš Smutka, Vojtěch Blažek, Ludmila Pánková, Kamil Maitah and Jiří Mach
Agronomy 2021, 11(11), 2344; https://doi.org/10.3390/agronomy11112344 - 19 Nov 2021
Cited by 9 | Viewed by 2511
Abstract
Machine learning algorithms have been applied in the agriculture field to forecast crop productivity. Previous studies mainly focused on the whole crop growth period while different time windows on yield prediction were still unknown. The entire growth period was separated into each month [...] Read more.
Machine learning algorithms have been applied in the agriculture field to forecast crop productivity. Previous studies mainly focused on the whole crop growth period while different time windows on yield prediction were still unknown. The entire growth period was separated into each month to assess their corresponding predictive ability by taking maize production (silage and grain) in Czechia. We present a thorough assessment of county-level maize yield prediction in Czechia using a machine learning algorithm (extreme learning machine (ELM)) and an extensive set of weather data and maize yields from 2002 to 2018. Results show that sunshine in June and water deficit in July were vastly influential factors for silage maize yield. The two primary climate parameters for grain maize yield are minimum temperature in September and water deficit in May. The average absolute relative deviation (AARD), root mean square error (RMSE), and coefficient (R2) of the proposed models are 6.565–32.148%, 1.006–1.071%, 0.641–0.716, respectively. Based on the results, silage yield will decrease by 1.367 t/ha (3.826% loss), and grain yield will increase by 0.337 t/ha (5.394% increase) when the max temperature in May increases by 2 °C. In conclusion, ELM models show a great potential application for predicting maize yield. Full article
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