Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

Sensitivity analysis of wheat yield based on growing degree days in different growth stages: : Application of machine learning approach enhanced by grey systems theory

Published: 01 July 2023 Publication History

Graphical abstract

Display Omitted

Highlights

The wheat yield sensitivity to different stages of growth and various variables has been investigated.
Growing degree days (GDD) and crop coefficient (Kc) were introduced as crop-specific variables.
The mid-season stage was identified as the best time window.
By using the proposed approach, wheat yield can be achieved before the harvest season.

Abstract

Cereals, especially wheat, have always been one of the main bases of the global food chain. Food security in the future will depend on more production or production with higher yields. So, it is critical to provide approaches to identify factors affecting wheat yield. Prior research has partially overcome the limitations of previous methods (field surveys, empirical statistical models, and crop models). A combination of climate and satellite inputs has been considered in previous research. But the main missing part is insufficient attention to crop-specific variables. A user-friendly interface is another issue that has been neglected because using satellite images requires time and expert effort. Here, to cover these gaps, the variables affecting the wheat yield in different stages of growth were identified and analyzed. The data of 17 years (2001–2017) from 31 provinces of Iran were used to evaluate the proposed approach. Six climatic variables (maximum and minimum temperature, relative humidity, wind speed, precipitation, and sunshine hours) from 238 weather stations and one variable for geographic yield bias (Ybaseline) were considered. Also, growing degree days (GDD) and crop coefficient (Kc) were introduced as crop-specific variables. First, the relative contribution of each variable was determined based on grey systems theory (GST) in different growth stages. Finally, the sensitivity analysis of input variables in each growth stage was performed based on eight scenarios and machine learning models, including artificial neural network (ANN), least squares support vector regression (LS-SVR), and adaptive neuro fuzzy inference system (NF). The results revealed that the mid-season stage suitable time window and the Kc, maximum temperature, relative humidity, Ybaseline, precipitation, and GDD with R2 = 0.958, WI = 0.989, and SI = 0.051 were the most influential variables in wheat yield estimation with ANN model. Estimating wheat yield before the harvest season can confirm the ability of the applied models in real-time applications.

References

[1]
S. Akinci, K. Abak, Determination of a suitable formula for the calculation of sum growing degree days in cucumber, I Int. Sympos. Cucurbits 492 (1997) 273–280.
[2]
Allen, R.G., Pereira, L.S., Raes, D., Smith, M., 1998. FAO Irrigation and drainage paper No. 56. Rome: Food and Agriculture Organization of the United Nations 56, e156.
[3]
R. Alvarez, Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach, Eur. J. Agron. 30 (2009) 70–77.
[4]
D. Ashourloo, M. Manafifard, M. Behifar, M. Kohandel, Wheat Yield Prediction based on Sentinel-2, Regression and Machine Learning Models in Hamedan, Iran, Scientia Iranica. (2022).
[5]
M. Babaee, S. Maroufpoor, M. Jalali, M. Zarei, A. Elbeltagi, Artificial intelligence approach to estimating rice yield, Irrig. Drain. (2021).
[6]
T. Chang, S.J. Lin, Grey relation analysis of carbon dioxide emissions from industrial production and energy uses in Taiwan, J. Environ. Manage. 56 (1999) 247–257.
[7]
Y. Chen, Z. Zhang, F. Tao, Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data, Eur. J. Agron. 101 (2018) 163–173.
[8]
M. Cheng, J. Penuelas, M.F. McCabe, C. Atzberger, X. Jiao, W. Wu, X. Jin, Combining multi-indicators with machine-learning algorithms for maize yield early prediction at the county-level in China, Agric. For. Meteorol. 323 (2022).
[9]
D.C. Corrales, C. Schoving, H. Raynal, P. Debaeke, E.-P. Journet, J. Constantin, A surrogate model based on feature selection techniques and regression learners to improve soybean yield prediction in southern France, Comput. Electron. Agric. 192 (2022).
[10]
N. Cristianini, J. Shawe-Taylor, An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, 2000.
[11]
N.K. David, Grey system and grey relational model, ACM SIGICE Bull. 20 (1994) 2–9.
[12]
Y.B. Dibike, S. Velickov, D. Solomatine, M.B. Abbott, Model induction with support vector machines: introduction and applications, J. Comput. Civ. Eng. 15 (2001) 208–216.
[13]
M. Elnesr, A. Alazba, An integral model to calculate the growing degree-days and heat units, a spreadsheet application, Comput. Electron. Agric. 124 (2016) 37–45.
[14]
H. Fang, S. Liang, G. Hoogenboom, J. Teasdale, M. Cavigelli, Corn-yield estimation through assimilation of remotely sensed data into the CSM-CERES-Maize model, Int. J. Remote Sens. 29 (2008) 3011–3032.
[15]
FAO, 2014. Food and Agriculture Organization of the United Nations Statistics, Rome, Italy, www.fao.org.
[16]
FAO, 2016. Crop production-State of Food and Agriculture (SOFA) report, www.fao.org.
[17]
P. Filippi, E.J. Jones, N.S. Wimalathunge, P.D. Somarathna, L.E. Pozza, S.U. Ugbaje, T.G. Jephcott, S.E. Paterson, B.M. Whelan, T.F. Bishop, An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning, Precis. Agric. 20 (2019) 1015–1029.
[18]
J.G. Fortin, F. Anctil, L.-É. Parent, M.A. Bolinder, Site-specific early season potato yield forecast by neural network in Eastern Canada, Precis. Agric. 12 (2011) 905–923.
[19]
D.V. Gaso, A.G. Berger, V.S. Ciganda, Predicting wheat grain yield and spatial variability at field scale using a simple regression or a crop model in conjunction with Landsat images, Comput. Electron. Agric. 159 (2019) 75–83.
[20]
E. Gilmore Jr, J. Rogers, Heat units as a method of measuring maturity in corn 1, Agron. J. 50 (1958) 611–615.
[21]
H.C.J. Godfray, J.R. Beddington, I.R. Crute, L. Haddad, D. Lawrence, J.F. Muir, J. Pretty, S. Robinson, S.M. Thomas, C. Toulmin, Food security: the challenge of feeding 9 billion people, Science 327 (2010) 812–818.
[22]
D. Gómez, P. Salvador, J. Sanz, J.L. Casanova, Modelling wheat yield with antecedent information, satellite and climate data using machine learning methods in Mexico, Agric. For. Meteorol. 300 (2021).
[23]
D. Gómez, P. Salvador, J. Sanz, J.L. Casanova, Regional estimation of garlic yield using crop, satellite and climate data in mexico, Comput. Electron. Agric. 181 (2021).
[24]
V. Gómez-Escalonilla, O. Diancoumba, D. Traoré, E. Montero, M. Martín-Loeches, P. Martínez-Santos, Multiclass spatial predictions of borehole yield in southern Mali by means of machine learning classifiers, J. Hydrol.: Reg. Stud. 44 (2022).
[25]
A. Gonzalez-Sanchez, J. Frausto-Solis, W. Ojeda-Bustamante, Predictive ability of machine learning methods for massive crop yield prediction, Span. J. Agric. Res. 12 (2014) 313–328.
[26]
P.M. Gopal, R. Bhargavi, A novel approach for efficient crop yield prediction, Comput. Electron. Agric. 165 (2019).
[27]
J. Han, Z. Zhang, J. Cao, Y. Luo, L. Zhang, Z. Li, J. Zhang, Prediction of winter wheat yield based on multi-source data and machine learning in China, Remote Sens. (Basel) 12 (2020) 236.
[28]
M.W. Hoover, Some effects of temperature on the growth of southern peas, Proc. Am. Soc. Hortic. Sci (1955) 308–312.
[29]
H.J. Hortik, C.Y. Arnold, Temperature and the rate of development of sweet corn, Proc. Amer. Horti. Sci 69 (1965) 400–404.
[30]
IMAJ, 2019. Iran’s Ministry of Agriculture Jihad, Tehran, Iran, https://maj.ir.
[31]
J.-S. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEE Trans. Syst. Man Cybern. 23 (1993) 665–685.
[32]
Z. Ji, Y. Pan, X. Zhu, D. Zhang, J. Wang, A generalized model to predict large-scale crop yields integrating satellite-based vegetation index time series and phenology metrics, Ecol. Ind. 137 (2022).
[33]
B. Ji, Y. Sun, S. Yang, J. Wan, Artificial neural networks for rice yield prediction in mountainous regions, J. Agric. Sci. 145 (2007) 249–261.
[34]
H. Jiang, H. Hu, R. Zhong, J. Xu, J. Xu, J. Huang, S. Wang, Y. Ying, T. Lin, A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level, Glob. Chang. Biol. 26 (2020) 1754–1766.
[35]
E.J. Jones, T.F. Bishop, B.P. Malone, P.J. Hulme, B.M. Whelan, P. Filippi, Identifying causes of crop yield variability with interpretive machine learning, Comput. Electron. Agric. 192 (2022).
[36]
J.W. Jones, G. Hoogenboom, C.H. Porter, K.J. Boote, W.D. Batchelor, L. Hunt, P.W. Wilkens, U. Singh, A.J. Gijsman, J.T. Ritchie, The DSSAT cropping system model, Eur. J. Agron. 18 (2003) 235–265.
[37]
D. Julong, Introduction to grey system theory, J. Grey Syst. 1 (1989) 1–24.
[38]
E. Kamir, F. Waldner, Z. Hochman, Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods, ISPRS J. Photogramm. Remote Sens. 160 (2020) 124–135.
[39]
M. Karbasi, M. Jamei, A. Malik, O. Kisi, Z.M. Yaseen, Multi-steps drought forecasting in arid and humid climate environments: Development of integrative machine learning model, Agric Water Manag 281 (2023).
[40]
M. Kaul, R.L. Hill, C. Walthall, Artificial neural networks for corn and soybean yield prediction, Agr. Syst. 85 (2005) 1–18.
[41]
N. Kim, Y.-W. Lee, Machine learning approaches to corn yield estimation using satellite images and climate data: a case of Iowa State, J. Korean Soc. Surv. Geod. Photogramm. Cartogr. 34 (2016) 383–390.
[42]
P. Krishnan, R. Sharma, A. Dass, A. Kukreja, R. Srivastav, R.J. Singhal, K. Bandyopadhyay, K. Lal, K. Manjaiah, R. Chhokar, Web-based crop model: Web InfoCrop–Wheat to simulate the growth and yield of wheat, Comput. Electron. Agric. 127 (2016) 324–335.
[43]
K. Kuwata, R. Shibasaki, Estimating corn yield in the united states with modis evi and machine learning methods. ISPRS Ann, Photogramm. Remote Sens. Spat. Inf. Sci 3 (2016) 131–136.
[44]
M.-F. Li, X.-P. Tang, W. Wu, H.-B. Liu, General models for estimating daily global solar radiation for different solar radiation zones in mainland China, Energ. Conver. Manage. 70 (2013) 139–148.
[45]
D.B. Lobell, M.B. Burke, On the use of statistical models to predict crop yield responses to climate change, Agric. For. Meteorol. 150 (2010) 1443–1452.
[46]
A. Malik, M. Jamei, M. Ali, R. Prasad, M. Karbasi, Z.M. Yaseen, Multi-step daily forecasting of reference evapotranspiration for different climates of India: A modern multivariate complementary technique reinforced with ridge regression feature selection, Agric Water Manag 272 (2022).
[47]
A. Malik, Y. Tikhamarine, P. Sihag, S. Shahid, M. Jamei, M. Karbasi, Predicting daily soil temperature at multiple depths using hybrid machine learning models for a semi-arid region in Punjab, India, Environ. Sci. Pollut. Res. 29 (2022) 71270–71289.
[48]
F. Mandariaga, Temperature summations in relation to lettuce growth, Proc. Amer. Soc. hort. Sci. (1951) 147–152.
[49]
C. Mariano, B. Monica, A random forest-based algorithm for data-intensive spatial interpolation in crop yield mapping, Comput. Electron. Agric. 184 (2021).
[50]
S. Maroufpoor, O. Bozorg-Haddad, E. Maroufpoor, Reference evapotranspiration estimating based on optimal input combination and hybrid artificial intelligent model: Hybridization of artificial neural network with grey wolf optimizer algorithm, J. Hydrol. 588 (2020).
[51]
S. Maroufpoor, O. Bozorg-Haddad, E. Maroufpoor, P.W. Gerbens-Leenes, H.A. Loáiciga, D. Savic, V.P. Singh, Optimal virtual water flows for improved food security in water-scarce countries, Sci. Rep. 11 (2021) 1–18.
[52]
T. Masters, Practical neural network recipes in C++, Academic Press Professional Inc., 1993.
[53]
S. Mishra, D. Mishra, G.H. Santra, Applications of machine learning techniques in agricultural crop production: a review paper, Indian J. Sci. Technol 9 (2016) 1–14.
[54]
N. Noureldin, M. Aboelghar, H. Saudy, A. Ali, Rice yield forecasting models using satellite imagery in Egypt, Egypt. J. Remote Sens. Space Sci. 16 (2013) 125–131.
[55]
X.E. Pantazi, D. Moshou, T. Alexandridis, R.L. Whetton, A.M. Mouazen, Wheat yield prediction using machine learning and advanced sensing techniques, Comput. Electron. Agric. 121 (2016) 57–65.
[56]
J.R. Porter, M. Gawith, Temperatures and the growth and development of wheat: a review, Eur. J. Agron. 10 (1999) 23–36.
[57]
A.K. Prasad, L. Chai, R.P. Singh, M. Kafatos, Crop yield estimation model for Iowa using remote sensing and surface parameters, Int. J. Appl. Earth Observ. Geoinform. 8 (2006) 26–33.
[58]
J.P. Resop, D.H. Fleisher, Q. Wang, D.J. Timlin, V.R. Reddy, Combining explanatory crop models with geospatial data for regional analyses of crop yield using field-scale modeling units, Comput. Electron. Agric. 89 (2012) 51–61.
[59]
Ruß, G., 2009. Data mining of agricultural yield data: A comparison of regression models, Industrial Conference on Data Mining. Springer, pp. 24-37.
[60]
Safa, B., Khalili, A., Teshnehlab, M., Liaghat, A., 2004. Artificial neural networks application to predict wheat yield using climatic data, Proceedings of 20th International Conference on IIPS. Iranian Meteorological Organization, pp. 1-39.
[61]
T. Sakamoto, M. Yokozawa, H. Toritani, M. Shibayama, N. Ishitsuka, H. Ohno, A crop phenology detection method using time-series MODIS data, Remote Sens. Environ. 96 (2005) 366–374.
[62]
L. Salazar, F. Kogan, L. Roytman, Use of remote sensing data for estimation of winter wheat yield in the United States, Int. J. Remote Sens. 28 (2007) 3795–3811.
[63]
P. Salvador, D. Gómez, J. Sanz, J.L. Casanova, Estimation of potato yield using satellite data at a municipal level: a machine learning approach, ISPRS Int. J. Geo Inf. 9 (2020) 343.
[64]
P. Samui, Support vector machine applied to settlement of shallow foundations on cohesionless soils, Comput. Geotech. 35 (2008) 419–427.
[65]
T. Searchinger, R. Heimlich, R.A. Houghton, F. Dong, A. Elobeid, J. Fabiosa, S. Tokgoz, D. Hayes, T.-H. Yu, Use of US croplands for biofuels increases greenhouse gases through emissions from land-use change, Science 319 (2008) 1238–1240.
[66]
A. Seyedzadeh, S. Maroufpoor, E. Maroufpoor, J. Shiri, O. Bozorg-Haddad, F. Gavazi, Artificial intelligence approach to estimate discharge of drip tape irrigation based on temperature and pressure, Agric Water Manag 228 (2020).
[67]
W. Shi, F. Tao, Z. Zhang, A review on statistical models for identifying climate contributions to crop yields, J. Geog. Sci. 23 (2013) 567–576.
[68]
B. Shiferaw, M. Smale, H.-J. Braun, E. Duveiller, M. Reynolds, G. Muricho, Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security, Food Security 5 (2013) 291–317.
[69]
Stas, M., Van Orshoven, J., Dong, Q., Heremans, S., Zhang, B., 2016. A comparison of machine learning algorithms for regional wheat yield prediction using NDVI time series of SPOT-VGT, 2016 fifth international conference on agro-geoinformatics (agro-geoinformatics). IEEE, pp. 1-5.
[70]
F. Tao, M. Yokozawa, Z. Zhang, Modelling the impacts of weather and climate variability on crop productivity over a large area: a new process-based model development, optimization, and uncertainties analysis, Agric. For. Meteorol. 149 (2009) 831–850.
[71]
F. Tao, Z. Zhang, W. Shi, Y. Liu, D. Xiao, S. Zhang, Z. Zhu, M. Wang, F. Liu, Single rice growth period was prolonged by cultivars shifts, but yield was damaged by climate change during 1981–2009 in C hina, and late rice was just opposite, Glob. Chang. Biol. 19 (2013) 3200–3209.
[72]
A. Tawafan, M.B. Sulaiman, Z.B. Ibrahim, Adaptive neural subtractive clustering fuzzy inference system for the detection of high impedance fault on distribution power system, IAES Int. J. Artificial Intell. 1 (2012) 63.
[73]
J. Van Wart, K.C. Kersebaum, S. Peng, M. Milner, K.G. Cassman, Estimating crop yield potential at regional to national scales, Field Crop Res 143 (2013) 34–43.
[74]
X. Wu, J. Shi, T. Zhang, Q. Zuo, L. Wang, X. Xue, A. Ben-Gal, Crop yield estimation and irrigation scheduling optimization using a root-weighted soil water availability based water production function, Field Crop Res 284 (2022).
[75]
Z. Zhang, X. Song, F. Tao, S. Zhang, W. Shi, Climate trends and crop production in China at county scale, 1980 to 2008, Theor. Appl. Climatol. 123 (2016) 291–302.
[76]
Y. Zhao, D.B. Lobell, Assessing the heterogeneity and persistence of farmers’ maize yield performance across the North China Plain, Field Crop Res 205 (2017) 55–66.
[77]
Y. Zhao, A.B. Potgieter, M. Zhang, B. Wu, G.L. Hammer, Predicting wheat yield at the field scale by combining high-resolution Sentinel-2 satellite imagery and crop modelling, Remote Sens. (Basel) 12 (2020) 1024.

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Computers and Electronics in Agriculture
Computers and Electronics in Agriculture  Volume 210, Issue C
Jul 2023
861 pages

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 July 2023

Author Tags

  1. Artificial Neural Network
  2. Crop yield
  3. Growing Degree Days
  4. Least Squares Support Vector Regression
  5. Neuro-Fuzzy

Qualifiers

  • Research-article

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 0
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 16 Nov 2024

Other Metrics

Citations

View Options

View options

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media