The Developing Blueberry Industry in China

Jia-feng, Jiang; Ji, Wei; Hónɡ, Yú; He, Shuqin

doi:10.5772/intechopen.88225

Cited by 7 publications

(5 citation statements)

References 31 publications

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“…Third, the Taylor expansion method is used to approximate the loss function thereby speeding up the process of optimization [52]. The predicted values of the tree ensemble model are computed using the formulae in Equations ( 4) and (5).…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…Bog blueberries are widely distributed and managed in the highlands of Northern China [4] as a major agroecosystem supporting the local rural economy. As of 2019, farmers in three provinces in Northeast China manage 3.5 million hectares (15% of the nationwide blueberry coverage) of bog blueberry with an estimated annual yield of 0.6 million metric tons (less than 5% of the national blueberry yield), according to the Heilongjiang Bureau of Agriculture and Forestry [5]. Increasing yield, fruit quality, and long-term economic stability in bog blueberry production requires better understanding of the fundamental ecological processes of cross-pollination as a result of various interacting factors.…”

Section: Introductionmentioning

confidence: 99%

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Parameterization and Calibration of Wild Blueberry Machine Learning Models to Predict Fruit-Set in the Northeast China Bog Blueberry Agroecosystem

Xiang

Obsie

et al. 2021

Agronomy

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Data deficiency prevents the development of reliable machine learning models for many agroecosystems, especially those characterized by a dearth of knowledge derived from field data. However, other similar agroecosystems with extensive data resources can be of use. We propose a new predictive modeling approach based upon the concept of transfer learning to solve the problem of data deficiency in predicting productivity of agroecosystems, where productivity is a nonlinear function of various interacting biotic and abiotic factors. We describe the process of building metamodels (machine learning models built and trained on simulation data) from simulations built for one agroecosystem (US wild blueberry) as the source domain, where the data resource is abundant. Metamodels are evaluated and the best metamodel representing the system dynamics is selected. The best metamodel is re-parameterized and calibrated to another agroecosystem (Northeast China bog blueberry) as the target domain where field collected data are lacking. Experimental results showed that our metamodel developed for wild blueberry achieved 78% accuracy in fruit-set prediction for bog blueberry. To demonstrate its usefulness, we applied this calibrated metamodel to investigate the response of bog blueberry to various weather conditions. We found that an 8% reduction in fruit-set of bog blueberry is likely to happen if weather becomes warmer and wetter as predicted by climate models. In addition, southern and eastern production regions will suffer more severe fruit-set decline than the other growing regions. Predictions also suggest that increasing commercially available honeybee densities to 18 bees/m2/min, or bumble bee densities to 0.6 bees/m2/min, is a viable way to compensate for the predicted 8% climate induced fruit-set decline in the future.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameterization and Calibration of Wild Blueberry Machine Learning Models to Predict Fruit-Set in the Northeast China Bog Blueberry Agroecosystem

Xiang

Obsie

et al. 2021

Agronomy

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“…The region has emerged as a key hub for early fresh blueberry production, gaining recognition for its highquality berries in China (Jiafeng et al, 2019). However, in 2020, a policy proposed to ensure grain production in China rmly prohibited any use of cropland for non-agricultural purposes, such that fruit production was forbidden on basic farmland.…”

Section: Introductionmentioning

confidence: 99%

Soil microbial community and chemical properties response to blueberry–soybean intercropping system

Ma,

Li,

Zhang

et al. 2024

Preprint

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Background and Aims Current global population growth and agricultural land resource limitations have led to intensifying conflicts between grain and fruit production. Methods we designed a potted blueberry–soybean intercropping system to evaluate its impacts on crop yield, disease occurrence, and soil microbial community composition using survey statistics, high-throughput sequencing, and correlation analysis. Results The results demonstrate that the system is a feasible solution for obtaining additional soybean yield. Blueberry pot soil (BPS) sampled and rhizosphere soil sampled from intercropped Huayan 1 soybean plants (HYS) showed significantly higher fungal and bacterial diversity than control bulk soil (CK) with no cultivation history. Microbial communities and unique OTUs were differentially enriched in BPS and HYS, respectively, and the latter effect was more pronounced. pH, organic matter, and total N were the main factors driving soil chemistry-mediated microbial differences in the community between CK and both HYS and BPS. The significantly lower microbial abundance in BPS was likely related to N fixation, whereas significantly enriched bacteria in HYS were related to the N regulatory protein C protein family, N regulatory IIA and P-II2 proteins, N fixation regulation proteins, and other N-related functions (p< 0.05), indicating that blueberry–soybean intercropping significantly improves microbial function in the soil. Conclusion These findings demonstrate that intercropping system could improve the acidification of soil and reduce the depletion of soil functional microorganisms caused by continuous monoculture of blueberries. Intercropping could help coordinated development of grain and fruit production, particularly in regions facing both food shortages and limited arable land in the world.

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“…Vaccinium uliginosum L. (bog bilberry) is a perennial deciduous shrub inhabiting peatlands and moorlands in temperate and boreal tundras (Jiang et al, 2019). Its populations can also be distributed in the understory layer of temperate forests dominated by birch (Betula nana) (Ejankowski, 2010) and black spruce (Picea mariana) (Rohrs-Richey and Mulder, 2007;Duan et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Interactive climate-soil forces shape the spatial distribution of foliar N:P stoichiometry in Vaccinium uliginosum planted in agroforests of Northeast China

Duan

Guo²,

Zhang³

et al. 2022

Front. Ecol. Evol.

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In regions with a ban on forest logging, fruit-bearing shrubs are favored as an alternative source of ecological products over the harvesting of timber. The introduction of cultured shrubs from their habitat to newly developed lands has to be decided according to nutrient availability. Foliar nitrogen (N) and phosphorus (P) stoichiometry is an easily measured and reliable parameter to quickly indicate possible limits in imbalanced N-P availability. When attempting to create a spatial distribution map of the foliar N:P ratio in an objective shrub species, it is helpful to first explore its potential acclimation to the N:P imbalance caused by the joint forces of soil property and regional climate. This study evaluated the cultivated populations of Vaccinium uliginosum in northeastern China's agroforests, using Vaccinium uliginosum as a model shrub species. A total of 51 populations were selected from 51 managed stands, of which 34 were in forests and 17 on farmlands. Foliar N and P concentrations, soil physical and chemical properties, and topography were investigated in 2018, and regional climatic factors were assessed by averaging previous 5-year records (2013–2018). V. uliginosum was determined to have a foliar N:P ratio lower than 4.4, which can be characterized as a limit of N relative to that of P. On forested lands, soil pH negatively impacted regressed foliar N:P, which was also part of the contributions of soil total P content and average temperature to foliar N concentration. On farmlands, low soil pH also resulted in a reduced foliar N:P ratio with joint contributions of ammonium N, nitrate N, and available P contents in soils and air humidity. Spatial interpolation indicated that western forests could benefit from introduced V. uliginosum with a higher foliar N concentration, while the introduction to eastern farmlands can lead to a higher foliar N:P ratio up to 14.6. Our study demonstrates recommended locations with expected soil and meteorological conditions by mapping spatial distributions, which can be referred to by other species and regions.

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The Developing Blueberry Industry in China

Cited by 7 publications

References 31 publications

Parameterization and Calibration of Wild Blueberry Machine Learning Models to Predict Fruit-Set in the Northeast China Bog Blueberry Agroecosystem

Parameterization and Calibration of Wild Blueberry Machine Learning Models to Predict Fruit-Set in the Northeast China Bog Blueberry Agroecosystem

Soil microbial community and chemical properties response to blueberry–soybean intercropping system

Interactive climate-soil forces shape the spatial distribution of foliar N:P stoichiometry in Vaccinium uliginosum planted in agroforests of Northeast China

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