Coupling effects of water and fertilizer on yield, water and fertilizer use efficiency of drip-fertigated cotton in northern Xinjiang, China

Wang, Haidong; Wu, Lifeng; Cheng, Minghui; Fan, Junliang; Zhang, Fucang; Zou, Yufeng; Chau, Henry Wai; Zhi-jian, Gao; Wang, Xiukang

doi:10.1016/j.fcr.2018.02.002

Cited by 195 publications

(88 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…), and are largely influenced by climate (such as water, heat, etc.) [22][23][24][25][26]. Many studies have shown that irrigation and fertilization influence nitrate-N leaching in farmland ecosystems [27,28].…”

Section: Discussionmentioning

confidence: 99%

“…Appropriate irrigation can be achieved by improving the harvest index to both increase yield and IWUE [35,36]. Within a certain range of water and fertilizer inputs, the yield increased as irrigation and fertilization increased, but the yield decreased when the irrigation and fertilization exceeded a certain threshold [25]. Therefore, reasonable water and fertilizer inputs could achieve high yields [38,39].…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Interactive Effects of Water and Fertilizer on Yield, Soil Water and Nitrate Dynamics of Young Apple Tree in Semiarid Region of Northwest China

et al. 2019

Self Cite

View full text Add to dashboard Cite

Exploring the interactive effect of water and fertilizer on yield, soil water and nitrate dynamics of young apple tree is of great importance to improve the management of irrigation and fertilization in the apple-growing region of semiarid northwest China. A two-year pot experiment was conducted in a mobile rainproof shelter of the water-saving irrigation experimental station in Northwest A&F University, and the investigation evaluated the response of soil water and fertilizer migration, crop water productivity (CWP), irrigation water use efficiency (IWUE), partial factor productivity (PFP) of young apple tree to different water and fertilizer regimes (four levels of soil water: 75%–85%, 65%–75%, 55%–65% and 45%–55% of field capacity, designated W1, W2, W3 and W4, respectively; three levels of N-P2O5-K2O fertilizer, 30-30-10, 20-20-10 and 10-10-10 g plant−1, designated F1, F2 and F3, respectively). Results showed that F1W1, F2W1 and F3W1 had the highest average soil water content at 0~90 cm compared with the other treatments. When fertilizer level was fixed, the average soil water content was gradually increased with increasing irrigation amount. For W1, W2, W3 and W4, high levels of water content were mainly distributed at 50~80 cm, 40~70 cm, 30~50 cm and 10~30 cm, respectively. There was no significant difference in soil water content at all fertilizer treatments. However, F1 and F2 significantly increased soil nitrate-N content by 146.3%~246.4% and 75.3%~151.5% compared with F3. The highest yield appeared at F1W1 treatment, but there was little difference between F1W1 and F2W2 treatment. F2W2 treatment decreased yield by 7.5%, but increased IWUE by 11.2% compared with F1W1 treatment. Meanwhile, the highest CWP appeared at F2W2 treatment in the two years. Thus, F2W2 treatment (soil moisture was controlled in 65–75% of field capacity, N-P2O5-K2O were controlled at 20-20-10 g·tree−1) reached the best water and fertilizer coupling mode and it was the optimum combinations of water and fertilizer saving.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Interactive Effects of Water and Fertilizer on Yield, Soil Water and Nitrate Dynamics of Young Apple Tree in Semiarid Region of Northwest China

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…Scientific irrigation methods and regimes can effectively promote crop growth and nutrient absorption [29] and control pests and diseases [30]. Drip irrigation can maintain the soil in a good condition suitable for crop growth and is conducive to high crop yield and water use efficiency (WUE) [31][32][33]. Studies have shown that under some irrigation conditions, the frequency of irrigation can significantly affect the growth and yield of tomato [34], pepper [35], and garlic [36].…”

Section: Introductionmentioning

confidence: 99%

Effect of Irrigation Level and Irrigation Frequency on the Growth of Mini Chinese Cabbage and Residual Soil Nitrate Nitrogen

et al. 2018

Self Cite

View full text Add to dashboard Cite

: Nitrogen (N) fertilizer is known to improve the quality and biomass of vegetables, but it is unclear how to manage the large amount of NO3-N that accumulates in the soil after vegetable harvest. In this study, we examined the influence of irrigation level and irrigation frequency on the growth and soil residual NO3-N of the catch crop mini Chinese cabbage (Brassica pekinensis) in a greenhouse. Using conventional border irrigation with adequate water supply as a control (CK), three irrigation levels (WH: 160% crop evapotranspiration (ETc), WM: 120% ETc and WL: 80% ETc) and three irrigation frequencies (intervals of F2: 2 days, F4: 4 days, and F8: 8 days) were assessed in 2014, 2015 and 2016 in northwest China. The results showed that the weight of the leaves and leaf stalks was the primary determinant of yield, and that these are the primary N-containing vegetative organs of the plants. At the same irrigation level, the total N content of the plants increased in the order F8 < F2 < F4. The trend in the total N content in the mini Chinese cabbage plants among different treatments was synchronized with the yield. The highest total N content in the plants was observed in the WMF4 treatment during all three years. The three-year averages of mini Chinese cabbage aboveground biomass, yield and water use efficiency (WUE) in the WMF4 treatment were 60%, 64.5% and 119.2% higher respectively than in the CK treatment. The residual NO3-N content in the soil in the WMF4 treatment was only 1.3% higher than that in the CK treatment. The total N uptake in the WMF4 treatment was 79.2% higher than that in the CK treatment, and the N loss in the WMF4 treatment was 46.3% lower than that in the CK treatment. Under these experimental conditions, the WMF4 treatment can be recommended as an appropriate irrigation regime for mini Chinese cabbage under fallow greenhouse management in northwest China.

show abstract

“…China is one of the largest cotton producers and importers in the world, and Xinjiang is the primary cotton-growing region in China [33,34]. The chosen study area was on a large agriculture region (6118.08 km 2 or 1, 511, 810 acres) in Shihezi (44 • 37 N, 85 • 42 E), Xinjiang Province, China.…”

Section: Study Area and Field Campaignsmentioning

confidence: 99%

Comparison of Machine Learning Regression Algorithms for Cotton Leaf Area Index Retrieval Using Sentinel-2 Spectral Bands

Mao

Meng

et al. 2019

Applied Sciences

View full text Add to dashboard Cite

Leaf area index (LAI) is a crucial crop biophysical parameter that has been widely used in a variety of fields. Five state-of-the-art machine learning regression algorithms (MLRAs), namely, artificial neural network (ANN), support vector regression (SVR), Gaussian process regression (GPR), random forest (RF) and gradient boosting regression tree (GBRT), have been used in the retrieval of cotton LAI with Sentinel-2 spectral bands. The performances of the five machine learning models are compared for better applications of MLRAs in remote sensing, since challenging problems remain in the selection of MLRAs for crop LAI retrieval, as well as the decision as to the optimal number for the training sample size and spectral bands to different MLRAs. A comprehensive evaluation was employed with respect to model accuracy, computational efficiency, sensitivity to training sample size and sensitivity to spectral bands. We conducted the comparison of five MLRAs in an agricultural area of Northwest China over three cotton seasons with the corresponding field campaigns for modeling and validation. Results show that the GBRT model outperforms the other models with respect to model accuracy in average ( R 2 ¯ = 0.854, R M S E ¯ = 0.674 and M A E ¯ = 0.456). SVR achieves the best performance in computational efficiency, which means it is fast to train, and to validate that it has great potentials to deliver near-real-time operational products for crop management. As for sensitivity to training sample size, GBRT behaves as the most robust model, and provides the best model accuracy on the average among the variations of training sample size, compared with other models ( R 2 ¯ = 0.884, R M S E ¯ = 0.615 and M A E ¯ = 0.452). Spectral bands sensitivity analysis with dCor (distance correlation), combined with the backward elimination approach, indicates that SVR, GPR and RF provide relatively robust performance to the spectral bands, while ANN outperforms the other models in terms of model accuracy on the average among the reduction of spectral bands ( R 2 ¯ = 0.881, R M S E ¯ = 0.625 and M A E ¯ = 0.480). A comprehensive evaluation indicates that GBRT is an appealing alternative for cotton LAI retrieval, except for its computational efficiency. Despite the different performance of the ML models, all models exhibited considerable potential for cotton LAI retrieval, which could offer accurate crop parameters information timely and accurately for crop fields management and agricultural production decisions.

show abstract

Coupling effects of water and fertilizer on yield, water and fertilizer use efficiency of drip-fertigated cotton in northern Xinjiang, China

Cited by 195 publications

References 36 publications

Interactive Effects of Water and Fertilizer on Yield, Soil Water and Nitrate Dynamics of Young Apple Tree in Semiarid Region of Northwest China

Interactive Effects of Water and Fertilizer on Yield, Soil Water and Nitrate Dynamics of Young Apple Tree in Semiarid Region of Northwest China

Effect of Irrigation Level and Irrigation Frequency on the Growth of Mini Chinese Cabbage and Residual Soil Nitrate Nitrogen

Comparison of Machine Learning Regression Algorithms for Cotton Leaf Area Index Retrieval Using Sentinel-2 Spectral Bands

Contact Info

Product

Resources

About