Peanut (<i>Arachis hypogaea</i> L.): A Prospective Legume Crop to Offer Multiple Health Benefits Under Changing Climate

Akram, Nudrat Aisha; Shafiq, Fahad; Ashraf, Muhammad

doi:10.1111/1541-4337.12383

Cited by 71 publications

(50 citation statements)

References 253 publications

(292 reference statements)

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“…Peanut (Arachis hypogaea L.) is one of the major global food legume and oilseed crops, and offers multiple benefits to meet human nutritional needs throughout the world [1,2]. As stated by FAO (2017), peanut crops are grown in 118 countries (or regions) around the world using approximately 28 million ha of land [3].…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

Quantitative Estimation of the Nutrient Uptake Requirements of Peanut

Xie

Wang

et al. 2020

Agronomy

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“…Flaxseeds contain Ω-3 fatty acid, αlinoleic acid (ALA) and other bioactive compounds that have been associated with improving the lipid profile (Machado et al, 2013). Almonds have also been found to contain phytosterols which on ingestion mimic the structural and functional properties of cholesterol and compete with low-density-lipoprotein-cholesterol (LDL-C) and contribute to reduced serum LDL-C within blood vessels (Akram et al, 2018). These features have been associated with improving the lipid profile and management of dyslipidemia for PCOS subjects.…”

Section: Conclusion:-mentioning

confidence: 99%

Development of Garcinia Cambogia Enriched Nutri Bite for the Management of Pcos

Ahmed¹

2020

IJAR

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“…Peanut ( Arachis hypogea L.) is widely cultivated due to its high nutritional value. The major constituents of peanut are oil (36–54%), protein (16–36%), and carbohydrates (10.5–20%) (Akram, Shafiq, & Ashraf, 2018; Davis et al, 2016). In addition, peanut is cholesterol free, contains 13 different vitamins (Isanga & Zhang, 2007) and 26 essential trace minerals (Ayoola, Adeyeye, & Onawumi, 2012; Mao, Srzednicki, & Driscoll, 2012).…”

Section: Introductionmentioning

confidence: 99%

A moisture content prediction model for deep bed peanut drying using support vector regression

Wang

Jin

et al. 2020

J Food Process Engineering

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In order to make the moisture content monitoring more convenient and rapid during peanut drying process, the drying characteristics of peanut were investigated and a real‐time SVR moisture content monitoring model was established in this paper. The results showed that hot air temperature, initial moisture content, airflow rate and the layer height were the key factors on peanut drying, and the peanut variety showed little effect on drying. The SVR model exhibited a good performance with R2: 0.91, RMSE: 4.38, and bias: −7.5e‐3. Compared with the results of linear regression models and multilayer perceptron model, SVR model showed a better performance. In addition, the SVR model was validated by the drying data of other three varieties of peanuts. And the relative errors between the predicted values by SVR model and the measured values were within 20%, which suggested that SVR was a promising modeling algorithm for peanut drying. Practical application Peanut drying is essential for peanut production due to its high moisture content at harvest (about 30–50% on the wet basis), which makes it susceptible to mildew, or even produces aflatoxins. Peanut has a special physiological structure. In the drying process, the peanut kernels are gradually shrunk, which makes the air layer volume between the shell and the kernel gradually increases, thus hindering mass and heat transfer and leading to its different drying characteristics from other grain and oil crops. Furthermore, it is necessary to monitor the moisture content changes of peanut during deep bed drying to ensure the drying uniformity and prevent energy waste. Therefore, the drying characteristics of peanuts were studied and a moisture content prediction model for deep bed drying was established to assist the actual drying process.

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Peanut (Arachis hypogaea L.): A Prospective Legume Crop to Offer Multiple Health Benefits Under Changing Climate

Cited by 71 publications

References 253 publications

Quantitative Estimation of the Nutrient Uptake Requirements of Peanut

Quantitative Estimation of the Nutrient Uptake Requirements of Peanut

Development of Garcinia Cambogia Enriched Nutri Bite for the Management of Pcos

A moisture content prediction model for deep bed peanut drying using support vector regression

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