crop model; fruit tree; growth simulation; tree age; yield forecasting; Food Science; Biochemistry; Ecology; Food Animals; Animal Science and Zoology; Agronomy and Crop Science; Plant Science
Abstract :
[en] Mathematical models have been widely employed for the simulation of growth dynamics of annual crops, thereby performing yield prediction, but not for fruit tree species such as jujube tree (Zizyphus jujuba). The objectives of this study were to investigate the potential use of a modified WOFOST model for predicting jujube yield by introducing tree age as a key parameter. The model was established using data collected from dedicated field experiments performed in 2016–2018. Simulated growth dynamics of dry weights of leaves, stems, fruits, total biomass and leaf area index (LAI) agreed well with measured values, showing root mean square error (RMSE) values of 0.143, 0.333, 0.366, 0.624 t ha−1 and 0.19, and R2 values of 0.947, 0.976, 0.985, 0.986 and 0.95, respectively. Simulated phenological development stages for emergence, anthesis and maturity were 2, 3 and 3 days earlier than the observed values, respectively. In addition, in order to predict the yields of trees with different ages, the weight of new organs (initial buds and roots) in each growing season was introduced as the initial total dry weight (TDWI), which was calculated as averaged, fitted and optimized values of trees with the same age. The results showed the evolution of the simulated LAI and yields profiled in response to the changes in TDWI. The modelling performance was significantly improved when it considered TDWI integrated with tree age, showing good global (R2≥0.856, RMSE≤0.68 t ha−1) and local accuracies (mean R2≥0.43, RMSE≤0.70 t ha−1). Furthermore, the optimized TDWI exhibited the highest precision, with globally validated R2 of 0.891 and RMSE of 0.591 t ha−1, and local mean R2 of 0.57 and RMSE of 0.66 t ha−1, respectively. The proposed model was not only verified with the confidence to accurately predict yields of jujube, but it can also provide a fundamental strategy for simulating the growth of other fruit trees.
Disciplines :
Agriculture & agronomy Computer science
Author, co-author :
BAI, Tie-cheng; TERRA Teaching and Research Centre, Gembloux Agro-Bio Tech, Liège University, Gembloux, Belgium ; Southern Xinjiang Research Center for Information Technology in Agriculture/College of Information Engineering, Tarim University, Alaer, China
WANG, Tao; Southern Xinjiang Research Center for Information Technology in Agriculture/College of Information Engineering, Tarim University, Alaer, China
ZHANG, Nan-nan; Southern Xinjiang Research Center for Information Technology in Agriculture/College of Information Engineering, Tarim University, Alaer, China
CHEN, You-qi; Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, China
Mercatoris, Benoît ; Université de Liège - ULiège > TERRA Research Centre > Biosystems Dynamics and Exchanges (BIODYNE)
Language :
English
Title :
Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model
Publication date :
March 2020
Journal title :
Journal of Integrative Agriculture
ISSN :
2095-3119
eISSN :
2352-3425
Publisher :
Editorial Department of Scientia Agricultura Sinica
This research was supported by the National Natural Science Foundation of China (41561088 and 61501314) and the Science & Technology Nova Program of Xinjiang Production and Construction Corps, China (2018CB020).
Aggelopoulou, A D, Bochtis, D, Fountas, S, Swain, K C, Gemtos, T A, Nanos, G D, Yield prediction in apple orchards based on image processing. Precision Agriculture 12 (2011), 448–456.
Alexandrov, V A, Eitzinger, J, The potential effect of climate change and elevated air carbon dioxide on agricultural crop production in central and southeastern Europe. Journal of Crop Improvement 13 (2005), 291–331.
Asseng, S, Ewert, F, Rosenzweig, C, Jones, J W, Hatfield, J L, Ruane, A C, Boote, K J, Thorburn, P J, Rötter, R P, Cammarano, D, Brisson, N, Basso, B, Martre, P, Aggarwal, P K, Angulo, C, Bertuzzi, P, Biernath, C, Challinor, A J, Doltra, J, Gayler, S, et al. Uncertainty in simulating wheat yields under climate change. Nature Climate Change 3 (2013), 827–832.
Bai, T, Zhang, N, Chen, Y, Mercatoris, B, Assessing the performance of the WOFOST model in simulating jujube fruit tree growth under different irrigation regimes. Sustainability, 11, 2019, 1466.
Bai, T, Zhang, N, Mercatoris, B, Chen, Y, Improving jujube fruit tree yield estimation at the field scale by assimilating a single landsat remotely-sensed LAI into the WOFOST model. Remote Sensing, 11, 2019, 1119.
Bai, T, Zhang, N, Mercatoris, B, Chen, Y, Jujube yield prediction method combining Landsat 8 Vegetation Index and the phenological length. Computers and Electronics in Agriculture 162 (2019), 1011–1027.
Baly, E C C, The kinetics of photosynthesis. Proceedings of the Royal Society of London (Series B: Biological Sciences) 117 (1935), 218–239.
Bickel, P J, Chen, A, A nonparametric view of network models and Newman–Girvan and other modularities. Proceedings of the National Academy of Sciences of the United States of America 106 (2009), 21068–21073.
Blanco, M, Ramos, F, Van Doorslaer, B, Martínez, P, Fumagalli, D, Ceglar, A, Fernández, F J, Climate change impacts on EU agriculture: A regionalized perspective taking into account market-driven adjustments. Agricultural Systems 156 (2017), 52–66.
Brisson, N, Gary, C, Justes, E, Roche, R, Mary, B, Ripoche, D, Zimmer, D, Sierra, J, Bertuzzi, P, Burger, P, Bussière, F, Cabidoche, Y M, Cellier, P, Debaeke, P, Gaudillère, J P, Hénault, C, Maraux, F, Seguin, B, Sinoquet, H, An overview of the crop model STICS. European Journal of Agronomy 18 (2003), 309–332.
Ceglar, A, van der Wijngaart, R, de Wit, A, Lecerf, R, Boogaard, H, Seguini, L, van den Berg, M, Toreti, A, Zampieri, M, Fumagalli, D, Baruth, B, Improving WOFOST model to simulate winter wheat phenology in Europe: Evaluation and effects on yield. Agricultural Systems 168 (2019), 168–180.
Cheng, Z, Meng, J, Wang, Y, Improving spring maize yield estimation at field scale by assimilating time-series HJ-1 CCD data into the WOFOST model using a new method with fast algorithms. Remote Sensing, 8, 2016, 303.
Confalonieri, R, Acutis, M, Bellocchi, G, Donatelli, M, Multimetric evaluation of the models WARM, CropSyst, and WOFOST for rice. Ecological Modelling 220 (2009), 1395–1410.
Curnel, Y, de Wit, A J W, Duveiller, G, Defourny, P, Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS experiment. Agricultural and Forest Meteorology 151 (2011), 1843–1855.
van Diepen, C A, Wolf, J, van Keulen, H, Rappoldt, C, WOFOST: A simulation model of crop production. Soil Use Management 5 (1989), 16–24.
Dobermann, A, Dawe, D, Roetter, R P, Cassman, K G, Reversal of rice yield decline in a long-term continuous cropping experiment. Agronomy Journal 92 (2000), 633–643.
Eitzinger, J, Trnka, M, Hösch, J, Žalud, Z, Dubrovský, M, Comparison of CERES, WOFOST and SWAP models in simulating soil water content during growing season under different soil conditions. Ecological Modelling 171 (2004), 223–246.
Ewert, F, Rötter, R P, Bindi, M, Webber, H, Trnka, M, Kersebaum, K C, Olesen, J E, van Ittersum, M K, Janssen, S, Rivington, M, Semenov, M A, Wallach, D, Porter, J R, Stewart, D, Verhagen, J, Gaiser, T, Palosuo, T, Tao, F, Nendel, C, Roggero, P P, et al. Crop modelling for integrated assessment of risk to food production from climate change. Environmental Modelling and Software 72 (2015), 287–303.
Gao, Q, Wu, C, Wang, M, The Jujube (Ziziphus Jujuba Mill.) fruit: A review of current knowledge of fruit composition and health benefits. Journal of Agricultural and Food Chemistry 61 (2013), 3351–3363.
Gilardelli, C, Confalonieri, R, Cappelli, G A, Bellocchi, G, Sensitivity of WOFOST-based modelling solutions to crop parameters under climate change. Ecological Modelling 368 (2018), 1–14.
He, T, Study on the configuration and light distribution characteristics in slope-land jujube plantation of north Shaanxi, Yangling. MSc thesis, 2010, Northwest A&F University, Yanglin, Shaanxi, China (in Chinese).
Holzworth, D P, Huth, N I, deVoil, P G, Zurcher, E J, Herrmann, N I, McLean, G, Chenu, K, van Oosterom, E J, Snow, V, Murphy, C, Moore, A D, Brown, H, Whish, J P M, Verrall, S, Fainges, J, Bell, L W, Peake, A S, Poulton, P L, Hochman, Z, Thorburn, P J, et al. APSIM — Evolution towards a new generation of agricultural systems simulation. Environmental Modelling and Software 62 (2014), 327–350.
Holzworth, D P, Snow, V, Janssen, S, Athanasiadis, I N, Donatelli, M, Hoogenboom, G, White, J W, Thorburn, P, Agricultural production systems modelling and software: Current status and future prospects. Environmental Modelling and Software 72 (2014), 276–286.
Huang, J, Ma, H, Sedano, F, Lewis, P, Liang, S, Wu, Q, Su, W, Zhang, X, Zhu, D, Evaluation of regional estimates of winter wheat yield by assimilating three remotely sensed reflectance datasets into the coupled WOFOST–PROSAIL model. European Journal of Agronomy 102 (2019), 1–13.
Huang, J, Ma, H, Su, W, Zhang, X, Huang, Y, Fan, J, Wu, W, Jointly assimilating MODIS LAI and ET products into the SWAP model for winter wheat yield estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (2015), 4060–4071.
Huang, J, Sedano, F, Huang, Y, Ma, H, Li, X, Liang, S, Tian, L, Zhang, X, Fan, J, Wu, W, Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation. Agricultural and Forest Meteorology 216 (2016), 188–202.
Huang, J, Tian, L, Liang, S, Ma, H, Becker-Reshef, I, Huang, Y, Su, W, Zhang, X, Zhu, D, Wu, W, Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agricultural and Forest Meteorology 204 (2015), 106–121.
Jones, J W, Hoogenboom, G, Porter, C H, Boote, K J, Batchelor, W D, Hunt, L A, Wilkens, P W, Singh, U, Gijsman, A J, Ritchie, J T, The DSSAT cropping system model. European Journal of Agronomy 18 (2003), 235–265.
Kroes, J G, Supit, I, Impact analysis of drought, water excess and salinity on grass production in the netherlands using historical and future climate data. Agriculture, Ecosystems and Environment 144 (2011), 370–381.
Li, J, Fan, L, Ding, S, Ding, X, Nutritional composition of five cultivars of Chinese jujube. Food Chemistry 103 (2007), 454–460.
Liu, F, Liu, X, Ding, C, Wu, L, The dynamic simulation of rice growth parameters under cadmium stress with the assimilation of multi-period spectral indices and crop model. Field Crops Research 183 (2015), 225–234.
Ma, G, Huang, J, Wu, W, Fan, J, Zou, J, Wu, S, Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield. Mathematical and Computer Modelling 58 (2013), 634–643.
Rahman, M M, Robson, A, Bristow, M, Exploring the potential of high resolution WorldView-3 imagery for estimating yield of mango. Remote Sensing, 10, 2018, 1866.
Reidsma, P, Ewert, F, Boogaard, H, van Diepen, K, Regional crop modelling in Europe: The impact of climatic conditions and farm characteristics on maize yields. Agricultural Systems 100 (2009), 51–60.
Reidsma, P, Wolf, J, Kanellopoulos, A, Schaap, B F, Mandryk, M, Verhagen, J, Van Ittersum, M K, Climate change impact and adaptation research requires integrated assessment and farming systems analysis: A case study in the Netherlands. Environmental Research Letters, 10, 2015, 045004.
Rötter, R, Van Keulen, H, Variations in yield response to fertilizer application in the tropics: II. Risks and opportunities for smallholders cultivating maize on Kenya's arable land. Agricultural Systems 53 (1997), 69–95.
Sun, L, Gao, F, Anderson, M C, Kustas, W P, Alsina, M M, Sanchez, L, Sams, B, McKee, L, Dulaney, W, White, W A, Alfieri, J G, Prueger, J H, Melton, F, Post, K, Daily mapping of 30 m LAI and NDVI for grape yield prediction in California vineyards. Remote Sensing, 9, 2017, 317.
Supit, I, Predicting national wheat yields using a crop simulation and trend models. Agricultural and Forest Meteorology 88 (1997), 199–214.
Supit, I, van Diepen, C A, de Wit, A J W, Kabat, P, Baruth, B, Ludwig, F, Assessing climate change effects on European crop yields using the crop growth monitoring system and a weather generator. Agricultural and Forest Meteorology 164 (2012), 96–111.
Thornley, J H M, Mathematical models in plant physiology. 1976, Academic Press, London [2018-12-10]. https://www.cabdirect.org/cabdirect/abstract/19760343677.
Todorovic, M, Albrizio, R, Zivotic, L, Abi Saab, M T, Stöckle, C, Steduto, P, Assessment of Aquacrop, Cropsyst, and WOFOST models in the simulation of sunflower growth under different water regimes. Agronomy Journal 101 (2009), 509–521.
Tripathy, R, Chaudhari, K N, Mukherjee, J, Ray, S S, Patel, N K, Panigrahy, S, Parihar, J S, Forecasting wheat yield in Punjab State of India by combining crop simulation model WOFOST and remotely sensed inputs. Remote Sensing Letters 4 (2013), 19–28.
Vanuytrecht, E, Thorburn, P J, Responses to atmospheric CO2 concentrations in crop simulation models: A review of current simple and semicomplex representations and options for model development. Global Change Biology 23 (2017), 1806–1820.
Van Walsum, P E V, Supit, I, Influence of ecohydrologic feedbacks from simulated crop growth on integrated regional hydrologic simulations under climate scenarios. Hydrology and Earth System Sciences 16 (2012), 1577–1593.
Wang, X, Williams, J R, Gassman, P W, Baffaut, C, Izaurralde, R C, Jeong, J, Kiniry, J R, EPIC and APEX: Model use, calibration, and validation. Transactions of the ASABE 55 (2012), 1447–1462.
de Wit, A, Baruth, B, Boogaard, H, Van Diepen, K, Van Kraalingen, D, Micale, F, Te Roller, J, Supit, I, Van Den Wijngaart, R, Using ERA-INTERIM for regional crop yield forecasting in Europe. Climate Research 44 (2010), 41–53.
de Wit, A, Boogaard, H, Fumagalli, D, Janssen, S, Knapen, R, van Kraalingen, D, Supit, I, van der Wijngaart, R, van Diepen, K, 25 years of the WOFOST cropping systems model. Agricultural Systems 168 (2019), 154–167.
de Wit, A, Boogaard, H L, Diepen, C A V, Spatial resolution of precipitation and radiation: The effect on regional crop yield forecasts. Agricultural and Forest Meteorology 135 (2005), 156–168.
de Wit, A, van Diepen, C A, Crop model data assimilation with the ensemble kalman filter for improving regional crop yield forecasts. Agricultural and Forest Meteorology 146 (2007), 38–56.
de Wit, A, van Diepen, C A, Crop growth modelling and crop yield forecasting using satellite-derived meteorological inputs. International Journal of Applied Earth Observation and Geoinformation 10 (2008), 414–425.
de Wit, A, Duveiller, G, Defourny, P, Estimating regional winter wheat yield with WOFOST through the assimilation of green area index retrieved from MODIS observations. Agricultural and Forest Meteorology 164 (2012), 39–52.
Wolf, J, Hessel, R, Boogaard, H L, De Wit, A, Akkermans, W, van Diepen, C A, Modelling winter wheat production across Europe with WOFOST — The effect of two new zonations and two newly calibrated model parameter sets. Ahuja, L R, Ma, L, (eds.) Methods of Introducing System Models into Agricultural Research, 2011, American Society of Agronomy. Crop Science Society of America, Soil Science Society of America https://doi.org/10.2134/advagricsystmodel2.c11.
Yang, W, Gao, J, Xu, C, The correlation analysis of leaf area index and yield of red jujube. Xinjiang Agricultural Sciences 49 (2012), 1397–1400 (in Chinese).
Ye, X, Sakai, K, Garciano, L O, Asada, S I, Sasao, A, Estimation of citrus yield from airborne hyperspectral images using a neural network model. Ecological Modelling 198 (2006), 426–432.
Ye, Z, Yu, Q, Comparison of a new model of light response of photosynthesis with traditional models. Journal of Shenyang Agricultural University 38 (2007), 771–775 (in Chinese).
Zaman, Q U, Schumann, A W, Hostler, H K, Estimation of citrus fruit yield using ultrasonically-sensed tree size. Applied Engineering in Agriculture 22 (2006), 39–44.
Zhang, Q, Bai, T, Wu, C, Investigation on yield and quality of jujube in different tree shapes in direct seeding and orchard construction. Northern Horticulture 4 (2013), 18–23 (in Chinese).
Zheng, Y, Li, Z, Xiao, J, Yuan, W, Yan, M, Li, T, Zhang, Z, Sources of uncertainty in gross primary productivity simulated by light use efficiency models: Model structure, parameters, input data, and spatial resolution. Agricultural and Forest Meteorology 263 (2018), 242–257.
Zhou, G, Liu, X, Liu, M, Assimilating remote sensing phenological information into the WOFOST model for rice growth simulation. Remote Sensing, 11, 2019, 268.
Zhou, R, Damerow, L, Sun, Y, Blanke, M M, Using colour features of cv. ‘Gala’ apple fruits in an orchard in image processing to predict yield. Precision Agriculture 13 (2012), 568–580.