Microclimate, yield, and income of a jujube–cotton agroforestry system in Xinjiang, China

Wang, Xiuyuan; Shen, Lei; Liu, Tingting; Wei, Wenwen; Zhang, Shuai; Li, Luhua; Zhang, Wei

doi:10.1016/j.indcrop.2022.114941

Cited by 19 publications

(7 citation statements)

References 51 publications

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“…The analysis of LER values revealed that the weedy plots exhibited the highest values, indicating a greater efficiency in land utilization within those particular conditions. Previous research has consistently demonstrated that the superiority of intercropping can be attributed to various factors, including distinct morphological properties and growth patterns, as well as the inherent ability of intercropping components to efficiently utilize vital resources such as soil, moisture, light, and essential nutrient elements [ 30 , 37 , 38 ]. Wang et al [ 38 ], reported that a jujube–cotton intercropping system had advantages in LER, yield, and income.…”

Section: Discussionmentioning

confidence: 99%

Enhancing cotton sustainability: Multi-factorial intercropping, irrigation, and weed effects on productivity, quality and physiology

Abdulkareem,

Mokhtassi-Bidgoli,

Ayyari

et al. 2024

Heliyon

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Section: Discussionmentioning

confidence: 99%

Enhancing cotton sustainability: Multi-factorial intercropping, irrigation, and weed effects on productivity, quality and physiology

Abdulkareem,

Mokhtassi-Bidgoli,

Ayyari

et al. 2024

Heliyon

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“…It is a typical alluvial fan oasis, with geographic coordinates ranging from 82°48′ to 83°40′E and 41°5′ to 41°56′N. The area has a continental arid climate with abundant solar radiation, low precipitation, high evaporation, signi cant temperature differences, long sunshine hours, and other favorable natural conditions conducive to the development of forestry and fruit cultivation [30].…”

Section: Study Areamentioning

confidence: 99%

Object-Oriented Multi-Scale Segmentation and Multi-Feature Fusion-Based Method for Identifying Typical Fruit Trees in Arid Regions Using Sentinel-1/2 Satellite Images

Liang,

Sawut,

Cui

et al. 2024

Preprint

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The rapid and accurate identification of fruit trees provides a foundation for dynamic monitoring of orchard planting areas and estimating orchard yields objectively. This study aims to evaluate the applicability of time series Sentinel-1/2 satellite data for fruit tree classification and to provide a new method for accurately extracting fruit tree categories. Therefore, the study area selected is the Tarim Basin, the most important fruit-growing region in northwest China. The main focus is on the identification of three major fruit tree species in this region. Time series Sentinel-1/2 satellite images acquired from the Google Earth Engine (GEE) platform are used for the study. A multi-scale segmentation approach is applied, and six categories of features including spectral, phenological, texture, polarization, vegetation index, and red edge index features are constructed. A total of 44 features are extracted and optimized using the Vi feature importance index to determine the best time phase. Based on this, an object-oriented segmentation combined with Random Forest (RF) method is used to identify fruit tree categories. The results are compared with three other commonly used traditional machine learning algorithms, including Classification and Regression Tree(CART), Gradient Boosting Decision Tree(GBDT), and Support Vector Machine(SVM)classification methods, to determine the best approach for fruit tree identification. The results show that: (1) the object-oriented segmentation method helps to improve the accuracy of fruit tree identification features; September satellite images provide the best time window for fruit tree identification, with spectral, phenological, and texture features contributing the most to fruit tree category identification; (2) the RF model has a higher accuracy in identifying fruit tree categories than other machine learning models, with an Overall Accuracy (OA) and Kappa Coefficient(KC) of 94.60% and 93.74% respectively, indicating that the combination of object-oriented segmentation and RF algorithm has great value and potential for fruit tree identification and classification. This method can be applied to large-scale fruit tree remote sensing classification and provide an effective technical means for monitoring fruit tree planting areas using medium to high-resolution remote sensing images.

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“…Intercropping can increase the utilization efficiency of resources and land, and it is an important means to guarantee food security, diversify cropping systems, and promote sustainable agricultural development [8,9]. In Xinjiang, farmers commonly intercrop jujube with cotton to improve land use patterns [10]. This planting system plays crucial roles in reducing wind erosion, adjusting the agricultural microclimate, improving environmental conditions, and providing economic benefits [11,12].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Cotton Row Configuration in Jujube–Cotton Intercropping Systems Improves Their Productivity, Net Effects, and Sustainability

Wang,

Chen,

Wang

et al. 2024

Agronomy

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The bare row spacing between young jujube trees reduces resource use efficiency. Planting cotton between rows of jujubes can improve the efficiency of light, heat, water, and temperature resources. However, it is not clear how many rows of cotton between the jujube rows would be the most suitable pattern. A field study with different cropping systems was performed to investigate the land equivalent ratio (LER), the competition of cotton to jujube, and the sustainability index. The treatments included (1) monoculture jujube, (2) monoculture cotton, (3) jujube intercropped with two rows of cotton (J/C2), (4) jujube intercropped with four rows of cotton (J/C4), and (5) jujube intercropped with six rows of cotton (J/C6). The results showed that the LER under the J/C2, J/C4, and J/C6 systems were 1.17, 1.30, and 1.28, respectively. The LER and total yield were higher under J/C2 than under the J/C4 and J/C6 treatments. The overyielding rate of cotton was increased, while those of jujube were decreased with increasing rows of cotton. The competition between cotton to jujube was less than 0. The net, complementarity, selection effect, and sustainability index were significantly higher under J/C4 and J/C6 than under J/C2, with J/C4, showing stronger net effects. Both complementarity and selection effects contributed to the intercropping yield advantages. Comprehensively considering yield, economic efficiency, sustainability index, land use efficiency, and net effect, J/C4 is one of the most productive and sustainable planting patterns of jujube–cotton intercropping system in southern Xinjiang, which is the more ideal pattern in arid and semi-arid regions.

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Microclimate, yield, and income of a jujube–cotton agroforestry system in Xinjiang, China

Cited by 19 publications

References 51 publications

Enhancing cotton sustainability: Multi-factorial intercropping, irrigation, and weed effects on productivity, quality and physiology

Enhancing cotton sustainability: Multi-factorial intercropping, irrigation, and weed effects on productivity, quality and physiology

Object-Oriented Multi-Scale Segmentation and Multi-Feature Fusion-Based Method for Identifying Typical Fruit Trees in Arid Regions Using Sentinel-1/2 Satellite Images

Optimizing Cotton Row Configuration in Jujube–Cotton Intercropping Systems Improves Their Productivity, Net Effects, and Sustainability

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