Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model

Bai, Tiecheng; Wang, Tao; Zhang, Nannan; Chen, Youqi; Mercatoris, Benoît

doi:10.1016/s2095-3119(19)62753-x

Cited by 20 publications

(14 citation statements)

References 52 publications

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“…This resulted in relatively moderate values of the model accuracy (average NRMSE were 41.4%). However, these results are in accordance with other studies used the WOFOST model for LAI simulation for different crops such as: wheat [47], jujube [8] and corn [48]. For the seasonal ETa predictions, an average difference of about 8 mm between estimated and e measured ETa has been found.…”

Section: Discussionsupporting

confidence: 90%

“…For example, in China, it has been used to simulate and predict the yield of perennial jujube. The incorporation of tree age has significantly improved the performance of the model [8]. Its performance has also been successfully tested to analyze the effect of yield variability between cropping seasons, on different soil types and in the context of climate change [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Performance Evaluation of the WOFOST Model for Estimating Evapotranspiration, Soil Water Content, Grain Yield and Total Above-Ground Biomass of Winter Wheat in Tensift Al Haouz (Morocco): Application to Yield Gap Estimation

Dewenam¹,

Er‐Raki

Ezzahar

et al. 2021

Agronomy

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The main goal of this investigation was to evaluate the potential of the WOFOST model for estimating leaf area index (LAI), actual evapotranspiration (ETa), soil moisture content (SM), above-ground biomass levels (TAGP) and grain yield (TWSO) of winter wheat in the semi-arid region of Tensift Al Haouz, Marrakech (central Morocco). An application for the estimation of the Yield Gap is also provided. The model was firstly calibrated based on three fields data during the 2002–2003 and 2003–2004 growing seasons, by using the WOFOST implementation in the Python Crop simulation Environment (PCSE) to optimize the different parameters that provide the minimum difference between the measured and simulated LAI, TAGP, TWSO, SM and ETa. Then, the model validation was performed based on the data from five other wheat fields. The results obtained showed a good performance of the WOFOST model for the estimation of LAI during both growing seasons on all validation fields. The average R2, RSME and NRMSE were 91.4%, 0.57 m2/m2, and 41.4%, respectively. The simulated ETa dynamics also showed a good agreement with the observations by eddy covariance systems. Values of 60% and 72% for R2, 0.8 mm and 0.7 mm for RMSE, 54% and 31% for NRMSE are found for the two validation fields, respectively. The model’s ability to predict soil moisture content was also found to be satisfactory; the two validation fields gave R2 values equal to 48% and 49%, RMSE values equal to 0.03 cm3/cm3 and 0.05 cm3/cm3, NRMSE values equal to 11% and 19%. The calibrated model had a medium performance with respect to the simulation of TWSO (R2 = 42%, RSME = 512 kg/ha, NRMSE = 19%) and TAGP (R2 = 34% and RSME = 936 kg/ha, NRMSE = 16%). After accurate calibration and validation of the WOFOST model, it was used for analyzing the gap yield since this model is able to estimate the potential yield. The WOFOST model allowed a good simulation of the potential yield (7.75 t/ha) which is close to the optimum value of 6.270 t/ha in the region. Yield gap analysis reveals a difference of 5.35 t/ha on average between the observed yields and the potential yields calculated by WOFOST. Such difference is ascribable to many factors such as the crop cycle management, agricultural practices such as water and fertilization supply levels, etc. The various simulations (irrigation scenarios) showed that early sowing is more adequate than late sowing in saving water and obtaining adequate grain yield. Based on various simulations, it has been shown that the early sowing (mid to late December) is more adequate than late sowing with a total amount of water supply of about 430 mm and 322 kg (140 kg of N, 80 kg of P and 102 kg of K) of fertilization to achieve the potential yield. Consequently, the WOFOST model can be considered as a suitable tool for quantitative monitoring of winter wheat growth in the arid and semi-arid regions.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of the WOFOST Model for Estimating Evapotranspiration, Soil Water Content, Grain Yield and Total Above-Ground Biomass of Winter Wheat in Tensift Al Haouz (Morocco): Application to Yield Gap Estimation

Dewenam¹,

Er‐Raki

Ezzahar

et al. 2021

Agronomy

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“…It is mainly cultivated in Shandong, Hebei, Shanxi, Shaanxi, Henan provinces and Xinjiang Uygur Autonomous Region in China. Among them, Xinjiang has become the largest jujube producing area in China due to its special climatic environments [2,3]. Xinjiang is an arid area with a shortage of water resources, most crops in this area have been planted with advanced drip irrigation technology [4].…”

Section: Introductionmentioning

confidence: 99%

Adapting Root Distribution and Improving Water Use Efficiency via Drip Irrigation in a Jujube (Zizyphus jujube Mill.) Orchard after Long-Term Flood Irrigation

Zong

Wang

et al. 2021

Agriculture

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Jujube tree yields in dryland saline soils are restricted by water shortages and soil salinity. Converting traditional flood irrigation to drip irrigation would solve water deficit and salt stress. The root distribution reacts primarily to the availability of water and nutrients. However, there is little information about the response of jujube roots to the change from flood irrigation to drip irrigation. In this context, a two–year experiment was carried out to reveal the effects of the change from long–term flood irrigation to drip irrigation on soil water, root distribution, fruit yield, and water use efficiency (WUE) of jujube trees. In this study, drip irrigation amounts were designed with three levels, i.e., 880 mm (W1), 660 mm (W2), 440 mm (W3), and the flood irrigation of 1100 mm was designed as the control (CK). The results showed that replacing flood irrigation with drip irrigation significantly altered soil water distribution and increased soil moisture in the topsoil (0–40 cm). In the drip irrigation treatments with high levels, soil water storage in the 0–60 cm soil layer at the flowering and fruit setting, and fruit swelling stages of jujube trees increased significantly compared with the flood irrigation. After two consecutive years of drip irrigation, the treatments with higher irrigation levels increased root length density (RLD) in 0–60 cm soil depth but decreased that in the 60–100 cm depth. In the horizontal direction, higher irrigation levels increased RLD in the distance of 0–50 cm, while reducing RLD in the distance of 50–100 cm. However, the opposite conclusion was obtained in W3 treatment. Additionally, in the second year of drip irrigation, W2 treatment (660 mm) significantly improved yield and WUE, with an increasing of 7.6% for yield and 60.3% for WUE compared to the flood irrigation. In summary, converting flood irrigation to drip irrigation is useful in regulating root distribution and improving WUE, which would be a promising method in jujube cultivation in arid regions.

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“…The results of our research will provide a useful data source for their work. In addition, jujube trees are one of the most important economic crops in Xinjiang, and there are great similarities in the planting patterns and field divisions [3,4]. Several satellite imaging systems, such as Gaofen-1 wide field of view (WFV), Gaofen-6 WFV [56], and Sentinel-2 [57] with fine spatial, spectral, and temporal resolutions, have become freely available.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, Xinjiang, with its unique geographical location and abundant light and heat, produces one-half of jujube in China. Jujube production plays a significant role in the food supply and fruit export trade of Xinjiang and China as a whole [4].…”

Section: Introductionmentioning

confidence: 99%

Identification of Abandoned Jujube Fields Using Multi-Temporal High-Resolution Imagery and Machine Learning

Yang

Zhang

et al. 2021

Remote Sensing

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The jujube industry plays a very important role in the agricultural industrial structure of Xinjiang, China. In recent years, the abandonment of jujube fields has gradually emerged. It is critical to inventory the abandoned land soon after it is generated to adjust agricultural production better and prevent the negative impacts from the abandonment (such as outbreaks of diseases, insect pests, and fires). High-resolution multi-temporal satellite remote sensing images can be used to identify subtle differences among crops and provide a good tool for solving this problem. In this research, both field-based and pixel-based classification approaches using field boundaries were used to estimate the percentage of abandoned jujube fields with multi-temporal high spatial resolution satellite images (Gaofen-1 and Gaofen-6) and the Random Forest algorithm. The results showed that both approaches produced good classification results and similar distributions of abandoned fields. The overall accuracy was 91.1% for the field-based classification and 90.0% for the pixel-based classification, and the Kappa was 0.866 and 0.848 for the respective classifications. The areas of abandoned land detected in the field-based and pixel-based classification maps were 806.09 ha and 828.21 ha, respectively, accounting for 8.97% and 9.11% of the study area. In addition, feature importance evaluations of the two approaches showed that the overall importance of texture features was higher than that of vegetation indices and that 31 October and 10 November were important dates for abandoned land detection. The methodology proposed in this study will be useful for identifying abandoned jujube fields and have the potential for large-scale application.

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Growth simulation and yield prediction for perennial jujube fruit tree by integrating age into the WOFOST model

Cited by 20 publications

References 52 publications

Performance Evaluation of the WOFOST Model for Estimating Evapotranspiration, Soil Water Content, Grain Yield and Total Above-Ground Biomass of Winter Wheat in Tensift Al Haouz (Morocco): Application to Yield Gap Estimation

Performance Evaluation of the WOFOST Model for Estimating Evapotranspiration, Soil Water Content, Grain Yield and Total Above-Ground Biomass of Winter Wheat in Tensift Al Haouz (Morocco): Application to Yield Gap Estimation

Adapting Root Distribution and Improving Water Use Efficiency via Drip Irrigation in a Jujube (Zizyphus jujube Mill.) Orchard after Long-Term Flood Irrigation

Identification of Abandoned Jujube Fields Using Multi-Temporal High-Resolution Imagery and Machine Learning

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