Field prediction method for oil extraction rate of camellia oleifera forest

Abstract

The invention discloses a field prediction method for the oil extraction rate of a camellia oleifera forest, and the method comprises the following steps: S1, selecting a certain number of camellia oleifera forest sample plants which are 9-15 years old and have the same variety, collecting all fresh fruits on each sample plant, weighing the fresh fruits, and classifying the fresh fruits; s2, calculating the oil yield of the fresh fruits with different seed numbers according to the collected data; s3, the collected fresh fruits are grouped according to the number of fresh fruit seeds, the average oil extraction rate of the camellia oleifera forest is calculated based on the oil extraction rates of the fresh fruits with different seed numbers, the detection process is simple and easy to implement, forest farmers, managers and sales (or purchasers) can directly and rapidly predict in the field, and complex laboratory conditions are not needed.

Description

Technical Field

[0001] The invention relates to the technical field of oil-tea plantation management, and in particular to a field prediction method for the oil yield of an oil-tea plantation. Background Art

[0002] Camellia oleifera is a plant of the genus Camellia in the family Theaceae. It is a woody oil tree species unique to my country, and its seeds are rich in oil. The planting area will be 90 million mu in 2025. The oil production of Camellia oleifera forests has always been estimated based on experience, and there is a lack of quantitative measurement methods for oil yield. Therefore, it is necessary to study the rapid quantitative prediction of oil production in Camellia oleifera forests.

[0003] Studies have shown that the oil yield of camellia oleifera fruit is determined by mixed samples. When different researchers measure the same variety, the oil yield varies greatly, but the reasons for its influence are not clearly explained. The present invention has found that the oil content of a single fruit of camellia oleifera is positively correlated with the number of seeds. The oil yield of a camellia oleifera forest varies depending on the proportion of fresh fruits containing different numbers of seeds in the total fresh fruit. At the same time, the scientific nature of the cultivation and management methods also affects the oil content of fresh fruits. In the transaction process of camellia oleifera fruit, the selling price of camellia oleifera fruit in different parks is determined by the purchaser's previous years' oil yield or by weight pricing based on the development of camellia oleifera seeds. This lack of scientificity causes fruit farmers to be unwilling to invest in technology and affects the management enthusiasm of production. With the development of intensive and commercialized camellia oleifera, there are new characteristics and rules in the fruiting law between the newly planted camellia oleifera forest and the old camellia oleifera forest. Different climates and scientific cultivation and management also affect the oil yield of fruits. Therefore, a method for directly, quickly and conveniently detecting the oil yield of camellia oleifera is urgently needed in production. Summary of the invention

[0004] In view of the above-mentioned prior art, the present invention provides a field prediction method for the oil yield of oil-tea tree forests, mainly to solve the technical problems existing in the above-mentioned background technology.

[0005] To achieve the above object, the technical solution of the embodiment of the present invention is implemented as follows:

[0006] A method for predicting the oil yield of oil-tea camellia forest in the field, the method comprising the following steps:

[0007] S1. Select a certain number of oil-tea tree samples of the same variety with an age of 9 to 15 years, collect all fresh fruits from each sample, weigh them and classify them;

[0008] S2. Detect and calculate the oil yield of fresh fruits with different seed numbers based on the collected data;

[0009] S3. Group the collected fresh fruits according to the number of seeds in the fresh fruits, and calculate the average oil yield of the oil tea forest based on the oil yield of fresh fruits with different seed numbers.

[0010] Optionally, the number of single oil-tea trees of the same variety shall be ≥50.

[0011] Optionally, all fresh fruits on each sample plant are collected, weighed and classified, specifically including: collecting all fresh fruits on each oil tea tree and weighing them; when the fruit yield of a single plant is ≤1000g, selecting a single tree of the same variety with an age of 9-15 years in the oil tea forest as a replacement.

[0012] Optionally, all fresh fruits collected from the single-plate plant sample are mixed, and ≥1000 g of sample fruits are quantitatively and randomly weighed for testing from each plant.

[0013] Optionally, the sample fruit classification specifically includes: taking 7-10 100-300 ml containers, and placing the seeds of fresh fruits with the same number of seeds into the same container respectively.

[0014] Optionally, the oil yield of fresh fruits with different seed numbers is calculated by the following formula: fresh fruit oil yield = (dry kernel weight / fresh fruit weight) × dry kernel oil yield × 100%.

[0015] Optionally, the average oil yield of the oil-tea plantation is calculated by the following formula:

[0016]

[0017] Where n is fresh fruit with different number of seeds.

[0018] Optionally, fresh fruits with different seed numbers are dried at 40°C to constant weight and then weighed; kernels are taken out and weighed, with each kernel sample ≥40g, and the oil yield of the dry kernels is tested.

[0019] The beneficial effects of the present invention are as follows: the embodiments of the present application provide a set of standardized camellia oleifera oil yield prediction processes, which makes the prediction of camellia oleifera oil yield more scientific and reasonable, and reduces the uncertainty caused by previous reliance on experience and intuition; through the classification and detection of fresh fruits with different seed numbers, the oil yield characteristics of camellia oleifera fruits can be more accurately reflected, thereby improving the accuracy of the prediction results; and its detection process is simple and easy, so that forest farmers and managers can make rapid predictions directly in the field without the need for complex laboratory conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a schematic diagram of a flow chart of a method for predicting the oil yield of a tea oil forest in the field in an embodiment of the present application;

[0021] Figure 2 This is an external schematic diagram of a hierarchical screening tool in an embodiment of the present application;

[0022] Figure 3 This is an internal schematic diagram of the hierarchical screening tool in an embodiment of the present application. DETAILED DESCRIPTION

[0023] The technical solution of the present invention is further elaborated in detail below in conjunction with the drawings and specific embodiments of the specification. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. In the following description, the expression "some embodiments" is related to a subset of all possible embodiments, but it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0024] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features well known in the art are not described.

[0025] It should be understood that the present invention can be implemented in different forms and should not be interpreted as being limited to the embodiments proposed herein. On the contrary, providing these embodiments will make the disclosure thorough and complete, and the scope of the present invention will be fully conveyed to those skilled in the art. And the purpose of the terms used herein is only to describe specific embodiments and is not intended to be a limitation of the present invention. When used herein, the singular forms of "one", "one" and "said / the" are also intended to include plural forms, unless the context clearly indicates another way. It should also be understood that the terms "compose" and / or "include" when used in this specification determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0026] It should also be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "inside", "outside", "left", "right" and similar expressions used herein are for illustrative purposes only and are not intended to be the only implementation method.

[0027] In order to fully understand the present invention, a detailed structure will be presented in the following description to illustrate the technical solution proposed by the present invention. The optional embodiments of the present invention are described in detail as follows, but in addition to these detailed descriptions, the present invention may also have other implementations.

[0028] Please refer to the attached Figures 1 to 3 The present application provides a method for predicting the oil yield of oil-tea tree forests in the field, the method comprising the following steps:

[0029] S1. Select a certain number of oil-tea tree samples of the same variety with an age of 9 to 15 years, collect all fresh fruits from each sample, weigh them and classify them;

[0030] S2. Calculate the oil yield of fresh fruits with different seed numbers based on the collected data;

[0031] S3. Group the collected fresh fruits according to the number of seeds in the fresh fruits, and calculate the average oil yield of the oil tea forest based on the oil yield of fresh fruits with different seed numbers.

[0032] Furthermore, when selecting forest stands: select oil tea forests with a tree age of 9-15 years, and the number of individual trees of the same variety should be no less than 50.

[0033] Healthy and strong tea oil trees are selected as sample trees, and the number of sample trees is usually between 30 and 50.

[0034] Furthermore, all fresh fruits from each sample plant were collected, weighed and classified, specifically including: all fresh fruits from each oil-tea tree were collected and weighed; when the fruit yield of a single plant was ≤1000g, a single plant of the same variety with an age of 9-15 years was selected as a substitute, all fresh fruits collected from the single-board sample plants were mixed, and ≥1000g of sample fruits were randomly weighed for each plant for testing.

[0035] join Figures 2 to 3 Furthermore, the fresh fruit is screened by a grading screening tool, wherein a grading screen plate is made according to the requirements of the grading holes, and the screen plate material can be made of acrylic plate, cardboard, hard iron wire, etc.; the hole can be a square hole or a round hole.

[0036] When collecting and processing fruit samples, collect all fresh fruits from each sample tree and weigh them. If the fruit yield per tree is less than 1,000 grams, the sample tree needs to be replaced. Then measure the fresh weight, seed weight (accuracy to 0.01 grams), horizontal and vertical diameters (accuracy to 0.01 mm), and seed number of each sample fruit.

[0037] Furthermore, when sorting the sample fruits, take 7-10 100-300 ml containers, which can be disposable cups or small boxes, and label them (1 seed, 2 seeds, 3 seeds...) and put the seeds of fresh fruits with the same number of seeds into the same container.

[0038] After completing the sample fruit classification, sample seed processing is also required, which specifically includes: mixing seeds with the same number of seeds evenly. If the weight is less than 750 grams, add single fruits with the same number of seeds until the required weight is reached.

[0039] Divide the sample into 3-5 portions, pack each portion in a bag (such as an envelope or insect net), dry to constant weight at no more than 40°C, then weigh, remove the kernels and weigh again. Each kernel sample should weigh at least 40 grams and be tested for oil content.

[0040] Furthermore, the oil yield of fresh fruits with different seed numbers was calculated by the following formula: fresh fruit oil yield = (dry kernel weight / fresh fruit weight) × dry kernel oil yield × 100%.

[0041] Furthermore, the average oil yield of the oil-tea plantation is calculated by the following formula:

[0042]

[0043] Where n is fresh fruit with different number of seeds.

[0044] In order to verify the method proposed in this application, the following experimental process is given. First, the significance of the difference in oil yield of fresh fruits with different seed numbers of the same variety is analyzed, and those with no significant difference at the p = 0.05 level are divided into the same group; then the significance of the difference in horizontal diameter of fresh fruits with different seed numbers of the same variety is analyzed, and those with no significant difference at the p = 0.05 level are combined into the same group; based on the above reasons, the number of seeds in fresh fruits is divided into 6 levels: 1, 2, 3, 4-5, 6, and 7.

[0045] The calculation method for the oil yield of level 4-5 is: Oil yield = ((number of 4 seeds / number of 4+5 seeds) × oil yield of fresh fruit with 4 seeds + (number of 5 seeds / number of 4+5 seeds) × oil yield of fresh fruit with 5 seeds) / 2, and the horizontal diameter takes the larger value among levels 4-5.

[0046] The sample plants and fruits were processed, and the following table was obtained:

[0047] Table 1 Comparison of oil content and transverse diameter of fresh fruits with different seed numbers

[0048]

[0049]

[0050] 1) Sample selection: Mix the fresh fruits, take 5-7 sample plants, collect the fruits on all the sample plants, or collect the mixed fresh fruits collected by forest farmers. Take multiple sample fruits weighing ≥1000g and pass them through the grading sieve; when sieving, move the sieve left and right for more than 3-5 times. If necessary, spread the fresh fruits on different grade plates evenly, and sieve them 3-5 times again to allow the fruits smaller than the aperture to pass completely;

[0051] (2) Sample statistics: Count the total number of fruit samples for each sample and the number of fruit samples of different grades;

[0052] (3) Calculate the oil yield of the forest stand, which is the average value of 5-7 samples;

[0053] The quantities obtained are as follows:

[0054] Number of fresh fruits per seed: 500;

[0055] 2-seed fresh fruit number: 800;

[0056] 3-seed fresh fruit number: 700;

[0057] Number of fresh fruits with 4-5 seeds: 1000 (500 with 4 seeds and 500 with 5 seeds); because the difference in oil yield between 4-5 seeds was not significant, they were calculated together.

[0058] 6-seed fresh fruit number: 300;

[0059] 7-seed fresh fruit number: 200;

[0060] Total number of fresh fruits: 3500;

[0061] Oil yield of 1 seed fresh fruit: 3.56%;

[0062] Oil yield of 2-seed fresh fruit: 4.80%;

[0063] Oil yield of 3-seed fresh fruit: 6.59%;

[0064] Oil yield of fresh fruit with 4-5 seeds: (8.75%*500+10.44%*500) / (500+500)=9.60%;

[0065] Oil yield of 6-seed fresh fruit: 8.92%;

[0066] Oil yield of 7-seed fresh fruit: 7.60%;

[0067] (4) Average oil yield of oil tea forest = (number of fresh fruits with 1 seed / total number of fresh fruits)*oil yield of fresh fruits with 1 seed + (number of fresh fruits with 2 seeds / total number of fresh fruits)*oil yield of fresh fruits with 2 seeds + .... (number of fresh fruits with n seeds / total number of fresh fruits)*oil yield of fresh fruits with n seeds;

[0068] The average oil yield of oil tea forest is calculated as follows:

[0069] Average oil yield of oil tea forest = (500 / 3500*3.56%) + (800 / 3500*4.86%) + (700 / 3500*6.59%) + (1000 / 3500*9.60%) + (300 / 3500*8.92%) + (200 / 3500*7.60%) = (0.142857×3.56%) + (0.228571×4.80%) + (0.200 000×6.59%)+(0.285714×9.60%)+(0.085714×8.92%)+(0.057143×7.60%)=0.507142857%+1.097142857%+1.318000000%+2.742857143%+0.761428571%+0.433928571%=6.860000000%

[0070] Therefore, the average oil yield of this oil tea forest is 6.86%.

[0071] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. The protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for predicting the oil yield of oil-tea camellia forest in the field, characterized in that: The method comprises the following steps: S1. Select a certain number of oil-tea tree samples of the same variety with an age of 9 to 15 years, collect all fresh fruits from each sample, weigh them and classify them; S2. Detect and calculate the oil yield of fresh fruits with different seed numbers based on the collected data; S3. Group the collected fresh fruits according to the number of seeds in the fresh fruits, and calculate the average oil yield of the oil tea forest based on the oil yield of fresh fruits with different seed numbers.

2. The method for predicting the oil yield of a tea tree in the field according to claim 1, characterized in that: The number of single oil tea trees of the same variety is ≥50.

3. The method for predicting the oil yield of a tea tree forest in the field according to claim 1, characterized in that: All fresh fruits on each sample plant were collected, weighed and classified, including: collecting all fresh fruits of each oil-tea tree and weighing them; when the fruit yield of a single plant was ≤1000g, a single plant of the same variety with an age of 9-15 years was selected as a replacement.

4. The method for predicting the oil yield of oil-tea camellia forest in the field according to claim 1, characterized in that: Use grading plates to screen fresh fruits of different sizes.

5. The method for predicting the oil yield of oil-tea camellia forest in the field according to claim 1, characterized in that: The specific method of fruit sample classification includes: taking 7-10 100-300 ml containers, and placing seeds with the same number of seeds into the same container.

6. The method for predicting the oil yield of oil-tea camellia forest in the field according to claim 1, characterized in that: The oil yield of fresh fruits with different seed numbers was calculated by the following formula: fresh fruit oil yield = (dry kernel weight / fresh fruit weight) × dry kernel oil yield × 100%.

7. The method for predicting the oil yield of oil-tea camellia forest in the field according to claim 6, characterized in that: The average oil yield of the oil-tea plantation is calculated by the following formula: Where n is fresh fruit with different number of seeds.

8. The method for predicting the oil yield of oil-tea camellia forest in the field according to claim 6, characterized in that: Fresh fruits with different seed numbers were dried at 40°C until constant weight and then weighed; kernels were taken out and weighed, with each kernel sample ≥40g, and the oil yield of the dry kernels was tested.

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