Estimating nutrient uptake requirements for radish in China based on QUEFTS model

Zhang, Jiajia; He, Ping; Ding, Wencheng; Xu, Xinpeng; Ullah, Sami; Abbas, Tanveer; Ai, Chao; Li, Mingyue; Cui, Rongzong; Jin, Cuihong; Zhou, Wei

doi:10.1038/s41598-019-48149-6

Cited by 25 publications

(19 citation statements)

References 24 publications

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“…In the present study, the data with HI lower 0.4 was removed due to the fact that the crop yields had been subjected to different constraints, such as disease, pests, and drought, with the exception of N, P, or K supply limitations [23]. The parameters of a and d were calculated using data with HI ranging from 0.4 to 0.82 in order to ensure that the peanut growth had been mainly limited by nutrient constraints rather than biotic or abiotic stress conditions.…”

Section: Selection Of the Data For Adapting The Quefts Modelmentioning

confidence: 99%

“…The QUEFTS model has been successfully applied to various crops in a range of different countries with a variety of soil types and agricultural environments. For instance, the model has been applied to such crops as maize [10,15], rice [16,17], wheat [18,19], soybean [20,21], sweet potato [22], and radish [23]. The QUEFTS model considers the interactions between the N, P, and K for a certain crop yield targets, and has been effectively used in combination with site-specific nutrient management (SSNM) methods in order to determine fertilizer requirements within site-specific areas [16,24].…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Estimation of the Nutrient Uptake Requirements of Peanut

Xie

Wang

et al. 2020

Agronomy

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Understanding the characteristics of the balanced nutrient requirements for peanut to achieve target yields is paramount when formulating fertilizer management strategies to increase yields and avoid fertilizer loss. Nutritional requirement estimation models can provide effective alternatives for the estimation of the optimum crop balanced nutrient requirements under varied agricultural conditions which are less time consuming and expensive. In the present study, the quantitative estimation of the optimum crop balanced nutrient requirements of peanut in China were obtained using quantitative evaluation of fertility of tropical soils (QUEFTS) model. The database covered the main agro-ecological region for peanut crops in China between 1993 and 2018. The predicted results of the QUEFTS model indicated that nutrient uptake requirements increased linearly with increasing pod yields until the yields had reached approximately 60% to 70% of the potential pod yields. It was found that with the increasing pod yields during the nutrient linear absorption stage, the plants had required 38.4 kg N, 4.3 kg P, and 14.0 kg K in total to produce 1000 kg of pods, and the corresponding internal efficiencies were 26.0 kg N/kg, 235.0 kg P/kg, and 71.6 kg K/kg, respectively. In addition, the balance rates of the removal nutrient in the pods were determined to be 29.4 kg N, 2.9 kg P, and 4.9 kg K per 1000 kg of pod yield, or approximately 76.5%, 67.4%, and 34.7% of N, P, and K in the total plants, respectively. This study’s field validation experiments verified the applicability and accuracy of the QUEFTS model. Therefore, it was considered to be an effective alternative for the estimation of the optimal balance N, P, and K uptake requirements for peanut crops. These findings will potentially be helpful when making future decisions regarding fertilizer recommendations for peanut crops in China.

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Section: Selection Of the Data For Adapting The Quefts Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantitative Estimation of the Nutrient Uptake Requirements of Peanut

Xie

Wang

et al. 2020

Agronomy

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“…The constant a and d of N, P, and K were 265 and 731, 1935 and 6044, and 199 and 595 kg/kg for all watermelons (Table 6), respectively. Other crops, such as radish, showed that the constant a and d of N, P, and K were 241 and 845, 1069 and 4480, and 171 and 879 kg/kg, respectively [58]. The constants of a and d represented the internal efficiency at maximum accumulation and maximum dilution of a nutrient.…”

Section: Selection Of Data For Adapting the Quefts Modelmentioning

confidence: 99%

Estimation of Watermelon Nutrient Requirements Based on the QUEFTS Model

et al. 2020

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Estimating balanced nutrient requirements for a watermelon plantation is essential to increase its fruit yield and nutrient use efficiency. This is vital for China, which produces 60% of world’s watermelons with excessive fertilizer application. Therefore, datasets between 2000 and 2019 from field experiments in major watermelon producing regions across China were collected to assess relationships between fruit yield and nutrient uptake, and to estimate nitrogen (N), phosphorus (P), and potassium (K) requirements for a target yield using a modified Quantitative Evaluation of the Fertility of Tropical Soils (QUEFTS) model. The results showed that the QUEFTS model predicted a linear increase in fruit yield to 60–70% of the total potential yield when balanced amounts of N, P, and K nutrients were absorbed. To produce 1000 kg of watermelon, 2.11 kg N, 0.27 kg P, and 2.69 kg K were required in shoot, and the corresponding internal efficiencies (IE) were 475, 3682, and 372 kg fruit per kg of N, P, and K, respectively. The modified QUEFTS model also simulated a balanced N, P, and K removal by fruit (accounting for 50.9%, 58.2%, and 66.4% of these nutrient accumulations in shoots, respectively). Field validation experiments further verified that the modified QUEFTS model could be used for estimating balanced nutrient requirements. Results from this study can provide practical guidance on fertilizer recommendations for improving fruit yield while preventing excessive or deficient nutrient supplies in China’s watermelon plantations.

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“…Not only transfer of solutes, but also controlled-release fertilizers have also been objectives for simulation to have a clear understanding of the chemical and physiological processes (Shen et al 2015;Du et al 2004;Wang et al 1998). In addition, the nutrient uptake by plant roots has also simulated (Wu et al 2019;Zhang et al 2019;Louison et al 2015;Roose and Fowler 2004;Mankin and Fynn 1996). Historically, most simulations have focused on processes of individual nutrients occurring in soil, and little attention has been paid on mixed nutrients.…”

Section: Introductionmentioning

confidence: 99%