EA042275B1 - METHOD AND DEVICE FOR PREPARING SILAGE AND SILAGE - Google Patents

Description

The invention relates to a method for preparing silage with improved quality characteristics, to a silage process using this method or silage, as well as to a device for preparing an silage product with improved quality characteristics.

Prerequisites for the creation of the invention

Corn silage is one of the most widely used feeds in cattle diets in many parts of the world. Although the nutritional composition of silage, which is typically characterized by chemical composition, dry matter content, crude protein, starch, fiber and nutrient digestibility, is of paramount importance for optimizing animal performance and physiological condition, it is generally accepted that the quality of the fermentation during the ensiling process and its aerobic stability is also important. In fact, improperly fermented silage has a lower nutritional value and is often rejected by the animals, resulting in lower dry matter intake and reduced productivity and physiological condition. The quality of the fermentation that occurs during the ensiling process can be determined by analyzing the pH level and the concentration of a wide range of fermentation products such as lactate, acetate, propionate, butyrate, isobutyrate, ethanol, mannitol and ammonia. However, each of these parameters only provides information about a specific aspect of the fermentation. Thus, to assess the quality characteristics of two different fermentation processes, quality index scores, such as Flieg-Zimmer scores, German Agricultural Society (DLG) scores and Wanbell scores, or quality indices I1-I6, as previously described by Igino Andrighetto et al. (Andrighetto I. et al. (2018), Proposal and validation of new indices to assess the enzymatic quality of corn silage under ensiling laboratory conditions by analyzing receiver curves, Anim. Feed Sci. Techno., 242:31-40) to evaluate the quality of different fermentation processes, taking into account the value of various parameters.

However, these indices only provide information about the quality of the silage after fermentation and do not imply any changes or improvements to the ensiling process.

Thus, the object of the present invention was to provide a method that enables the farmer to predict whether freshly harvested crops, preferably maize, will be able to produce silage during the fermentation process. Such a method would allow the farmer to modify the ensiling process based on the chemical composition of freshly harvested crops to improve the quality of the ensiled product. Thus, the object of the present invention is to provide a method for determining the possibility of producing from freshly harvested crops, preferably corn, silage, or the need to add additional substances to the ensiling process to improve the fermentation of harvested crops.

Brief description of the invention

A first aspect of the present invention relates to a method for preparing an improved quality silage raw material to be ensiled, the method comprising the process of analyzing freshly harvested raw material to be ensiled to determine a plurality of parameters xi characterizing the chemical composition and/or properties of the harvested raw material;

applying the first model to the estimated parameters xi in order to determine the number Ini of the ensiling suitability index, which is characteristic of the expected ensiling suitability of the harvested raw material;

applying the second model to the estimated parameters xi to determine the retention index number Rei, which is representative of the expected dry matter loss of the harvested raw material during the ensiling process;

determining the types and amounts of ensiling additives to be added to the harvested raw material to control the ensiling process depending on the number Ini of the ensiling index and the Rei number of the retention index; and adding certain additives to the harvested raw material in order to produce silage raw material, in case the certain amount of silage additive exceeds zero.

Unlike the prior art evaluating the quality of the silage feedstock, the method of the present invention determines the potential of the harvested feedstock to be converted into good quality silage feedstock, thus enabling the prediction of an efficient fermentation process as well as the risk of dry matter loss during the ensiling process. Moreover, the method of the present invention provides the farmer with the possibility of optimizing the ensiling process by selecting appropriate ensiling additives added to influence the ensiling process. This is not possible in the case of already known quality index scores, such as Flieg-Zimmer and others, describing the quality of corn silage after the fermentation process; thus, it is not possible to accurately control the ensiling process to obtain improved silage.

Thus, the method of the invention uses two indices, namely (1) ensiling index number, also referred to as Ini; and (2) a retention index number, also referred to as Rei.

- 1 042275

The number Ini of the ensiling suitability index indicates the expected suitability of the harvested raw material for ensiling. Thus, it represents the relationship between the chemical composition of freshly harvested raw materials (expressed by the parameters xi) and the quality of the silage, i.e. its chemical composition. The latter can be expressed by quality indices known in the art, such as Flieg-Zimmer scores, German Agricultural Society (DLG) scores and Wanbell scores, or quality indices I1-I6 as previously described by Igino Andrighetto et al. (Andrighetto and etc., see above). Therefore, the ensiling index number correlates the chemical composition of the harvested raw material with the theoretical maximum quality index value that can be obtained for the harvested raw material after being converted to silage.

The Rei number of the retention index, on the other hand, indicates the expected dry matter loss (or expected dry matter retention) during the ensiling process. Thus, it represents the relationship between the chemical composition of freshly harvested raw materials (expressed as xi) and the dry matter content of the silage.

Accordingly, the method of the present invention takes into account both the expected quality of the chemical composition and the expected dry matter content of the ensiled silage to enable the farmer to positively influence the ensiling process when needed.

For the purposes of this description, the term harvested raw material or freshly harvested raw material refers to a post-harvest crop that has not been altered, including the addition of substances or modifications using processes that affect the chemical composition. The term silage raw material refers to the raw material that will be ensiled, i.e. to the raw material before ensiling. Thus, the silage feedstock is the sum of the harvested feedstock and any silage additives added to the harvested feedstock. In the absence of additives, silage raw material and harvested raw material are identical terms. This means that the harvested raw material is silage raw material if the indexes described herein indicate that the harvested raw material can be converted into proper silage without the addition of silage additives. However, if the indexes described here indicate that the harvested raw material cannot be converted into proper silage, then additives are added to the harvested raw material. In the latter case, the silage raw material is the sum of the collected raw materials and all additives added to it. In addition, the term silage refers to the product of the ensiling process, in particular the product of microbiological fermentation. Silage is the result of microbiological fermentation of silage raw materials, i.e. collected raw materials and all silage additives added to it if necessary.

The expression dry matter loss in the context of the present invention should be understood as the mass or mass difference [%] between the harvested raw material/silage and the ensiled product. It should not be understood solely as a composition or change in chemical composition [%]. For example, 10 kg of fresh sample (before ensiling) with a dry matter content of 30% means 3 kg of dry matter mass. In the same example, the sample after ensiling has a weight of 9 kg (loss of 1 kg due to leaching of liquid, consumption of sugar, gas, etc.), but in terms of chemical composition it has a 31% dry matter content, which corresponds to a mass of 2, 79 kg of dry matter. Thus, the chemistry in terms of dry matter content increases from 30 to 31%, but in terms of dry matter mass decreases from 3 to 2.79 kg, with a loss in terms of percentage dry matter mass of 7%.

Further, for the purposes of this description, the term "improved silage" refers to silage having a higher content of useful nutrients and/or a higher dry matter content after the influence of the ensiling process compared to silage not modified by the addition of silage additives, according to the present invention. after the influence of the ensiling process under the same conditions.

The harvested raw material can be any crop that can be subjected to an ensiling process, i.e. fermentation processes. Harvested raw materials can be, for example, cereals such as corn (Zea mays), sorghum, such as Sorghum bicolor and Sudanese, sugar cane, such as Saccharum officinarum, rye, such as Secale cereale L., barley, such as Hordeum vulgare, wheat, such as Triticum aestivum L., triticale, perennial chaff, eg Lolium perenne, alfalfa, eg Medicago sativa, and others. Of these, corn is of particular interest because corn silage is one of the most widely used feeds in cattle diets in many parts of the world, as it is an extremely productive crop that has excellent nutritional characteristics and a high probability of being preserved by ensiling.

Analysis process to determine parameters x; may use any method capable of providing information about the chemical composition and/or other property of the collected raw materials. In preferred embodiments, the analysis process may include a spectroscopic method, such as infrared spectroscopy (preferably near infrared spectroscopy), terahertz spectroscopy (preferably time-resolved terahertz spectroscopy), Raman spectroscopy, ultraviolet spectroscopy, mass spectroscopy,

- 2 042275 for example time-of-flight MALDI spectrometry, nuclear magnetic spectroscopy, laser breakdown excitation spectroscopy and others. Alternatively, the analysis process may include chromatographic techniques such as gas chromatography (GC), high performance liquid chromatography (HPLC), or others. In other examples, the analysis process may include thermal imaging techniques, wet-path analysis, and trace element analysis. One or more methods from each category can be combined to increase the amount of information on the collected raw materials. The preferred methods are fast, reliable and operational in the field. Spectroscopic methods are particularly useful here, preferably an infrared spectroscopy method, most preferably a near infrared spectroscopy (NIR) method, since the LIS allows a large number of parameters of interest to be determined in a single measurement.

The parameters xi determined at the stage of the analysis process may include any ingredient present in the harvested raw material, in particular those known to be affected or affected (positively or negatively) by fermentation processes during ensiling. Accordingly, the preferred parameters for selection may be water, dry matter (DM), ash (A), acid insoluble ash (AIA), crude protein (CP), mass concentration of sugars (TS), glucose (GLU), fructose ( FRU), sucrose (SAC), ether extract (EE), starch, cellulose, hemicellulose, lignin, crude fiber (CF), acid detergent fiber (ADF), neutral detergent fiber (NDF), and acid detergent lignin (ADL) . In addition, parameters characterizing other properties of the collected raw material, such as the pH value, can be determined. However, the parameters applicable in the present method are not limited to those mentioned above.

The number of parameters xi determined in the analysis process step is not particularly limited as long as the number is appropriate to obtain an ensilability index number and a retention index number. For example, the number of parameters xi determined in a step in the analysis process may range from 2 to 20, preferably 3 to 15, and even more preferably 4 to 10. The higher the number of parameters xi determined, the more predictable the numbers Ini and Rei will be. indexes. On the other hand, the complexity of the method increases as the number increases, so the number is generally a compromise between reliability and complexity.

The first model provides the possibility of obtaining the ensiling index number Ini as a function of the parameters xi. The first model may be a mathematical function, a lookup table, or the like, which is stored in a machine readable format.

According to a preferred embodiment of the invention, the first model used in the method for determining the number Ini of the ensiling suitability index is obtained by the correlation of the parameters xi, _e , which were determined as a result of measurements of test samples before ensiling, while the chemical composition of the silage was obtained after ensiling under specified conditions. In other words, the first model can be obtained empirically by performing experiments that include (a) collecting test samples of freshly harvested raw materials;

(b) determining the parameters xi, _e of test samples corresponding to the parameters xi determined at the stage of the analysis process;

(c) ensiling test specimens under specified conditions;

(d) determination of the chemical compositions of the silage of the test samples; and (e) correlating the chemical compositions of the silage with the parameters xi, _e of the test sample before ensiling, resulting in a first model.

Preferably, the correlation step (e) comprises assigning a quality index to the silage according to its chemical composition, such as Flieg-Zimmer (FZS) scores, German Agricultural Society (DLG) scores and Wanbell scores, or one of the quality indices I1 -I6 Igino Andrighetto et al. (see above) and the correlation of the quality index with the parameters xi, _e of the studied samples. The correlation step may include a mathematical regression analysis that will result in a mathematical function (including regression parameters) expressing the ensiling index number Ini as a function of the parameters xi.

The second model provides the possibility of obtaining the number Rei of the retention index as a function of the parameters xi. The second model may be a mathematical function, a lookup table, or the like, which is stored in a machine readable format.

According to a preferred embodiment of the invention, the second model used in the method for determining the retention index number is obtained by correlating the parameters xi, _e , which were determined as a result of measurements of test samples before ensiling, while data on the content of dry matter in the silage is obtained after ensiling in given conditions. In other words, the second model can be obtained empirically by performing experiments, including (a) collecting test samples of freshly harvested raw materials;

(b) determining the parameters x _i , _e test samples corresponding to the parameters xi determined

- 3 042275 at the stage of the analysis process;

(c) ensiling test specimens under specified conditions;

(d) determination of the dry matter content of the silage of the test samples; and (e) correlation of dry matter content in the silage with the parameters x _i , _e of the test sample before ensiling, resulting in a second model.

The correlation step may include a mathematical regression analysis that will result in a mathematical function (including regression parameters) expressing the index number Rei as a function of the parameters x _i .

Generally, the ensilability index number Ini and the retention index number Rei are independently a dimensionless number or a percentage.

According to a preferred embodiment of the invention, the step of determining the types and amounts of additives to be added to the harvested silage includes comparing the silage index number Ini with a first threshold value depending on whether the determined silage index number exceeds the first threshold value or not. reaches this value, the type and content of the additive selected from the first group of additives is determined; and comparing the retention index number Rei with the second threshold value, depending on whether the determined ensilability index number exceeds or does not reach the first threshold value, the type and content of the additive selected from the second additive group is determined.

According to one embodiment of the invention, the additives from the first and second groups of additives are selected from homofermentative and/or heterofermentative organisms, more preferably from Lactobacillus plantarum, Lactobacillus casei, Lactobacillus delbruckii subs, bulgaricus, Lb. acidophilus, Lb. helveticus, Streptococcum faecium, Streptococcus salivarius subsp. thermophilus; Streptococcus spp., Pediococcus pentosaceus, Lactobacillus curbatus, Lactobacillus coryniformis subs. coryniformis и Pediococcus acidilactici, энтерококков, педиококков и аэрококков, Lactobacillus brevis, Lactobacillus buchneri, Lactobacillus fermentum, Lactobacillus reuteri, Lactobacillus viridescens, Lactobacillus rhamnosus, Leuconstoc cremoris (Leuconostoc mesenteroides ssp. cremoris), Lactobacillus platarum, Lactobacillus casei, Lactobacillus curvatus и Leuconstoc dextranicum . Other additives of the first group may include one/or more sugars such as glucose, fructose, sucrose and their derivatives, one or more acid-forming compounds such as formic acid, propionic acid, lactic acid and/or water. Combinations of the aforementioned additives may also be used.

In a preferred embodiment of the invention, the first group of additives can be applied to the harvested raw material having a low ensilability index number. According to a preferred embodiment of the invention, the first group of additives preferably includes homofermentative microorganisms. For example, the first group of additives can be selected from inoculums of various bacterial strains, for example Lactobacillus plantarum, Lactobacillus casei, Lactobacillus delbruckii subs. bulgaricus, Lb. acidophilus, Lb. helveticus, Streptococcum faecium, Streptococcus salivarius subsp. thermophilus; Streptococcum spp., Pediococcus pentosaceus, Lactobacilus curbatus, Lactobacilus coryniformis subs. coryniformis and Pediococcus acidilactici, enterococci, pediococci and aerococci. Other additives of the first group may include one/or more sugars such as glucose, fructose, sucrose and their derivatives, one or more acid-forming compounds such as formic acid, propionic acid, lactic acid and/or water. Combinations of the aforementioned additives may also be used. As used herein, the term homofermentative microorganisms refers to lactic acid bacteria that ferment glucose with lactic acid as a major by-product. Homofermentative microorganisms are used in fermentation processes in which rapid formation of lactic acid and a lower pH are desirable.

According to another preferred embodiment of the invention, a second group of additives can be applied to the harvested stock with a low retention index number, i. e. to raw materials that are expected to undergo high dry matter losses during fermentation. According to another preferred embodiment, the second group of additives preferably includes heterofermentative microorganisms. For example, the second group of additives can be selected from inoculums of various bacterial strains, e.g. Lactobacillus brevis, Lactobacillus buchneri, Lactobacillus fermentum, Lactobacillus reuteri, Lactobacillus viridescens, Lactobacillus rhamnosus, Leuconstoc cremoris (Leuconostoc mesenteroides ssp. cremoris), Lactobacillus platarum, Lactobacillus casei and Leuconstoc dextranicum. Other additives of the second group may include one or more sugars such as glucose, fructose, sucrose and their derivatives, one or more acid-forming compounds such as formic acid, propionic acid, lactic acid and/or water. Combinations of the aforementioned additives may also be used. The term heterofermentative microorganisms as used herein refers to lactic acid bacteria that ferment glucose with lactic acid, ethanol/acetic acid and carbon dioxide (CO2) as by-products.

- 4 042275 products. Heterofermentative microorganisms are used in fermentation processes.

In the event that the harvested raw material has a low ensiling index number and a low retention index, additives of the first and second groups can be applied to the harvested raw material. In addition, if the harvested material is found to have a high ensilability index number and a high retention index, no additives will be applied.

It is especially preferred that the process be carried out in a continuous manner during the harvesting of the raw materials, i.e. in the field. In other words, according to this embodiment, the determination of ensiling index number and retention index, as well as the addition of additives, is carried out in the field as a harvesting process; thus, silage preparation is carried out in the field, and silage can be directly fed to the ensiling process.

Another object of the present invention relates to the process of ensiling, which includes the preparation of silage raw materials using the method according to the first object according to the present description; and fermentation of silage raw materials to obtain silage.

Another object of the present invention relates to a device for preparing silage raw materials of improved quality to be ensiled, the device includes an analysis tool for analyzing freshly harvested raw materials to determine a plurality of parameters xi characterizing the chemical composition and/or property of the harvested raw material;

a first model correlating certain parameters xi with an ensilability index number Ini that is indicative of the expected ensilability of the harvested raw material;

a second model correlating certain parameters xi with a retention index number Rei that is indicative of the expected dry matter loss of the harvested raw material during the ensiling process;

determining means for determining types and amounts of ensiling process additives that are added to the harvested raw material to control the ensiling process depending on the ensilability index number Ini and the retention index number Rei; and a supply means for adding certain additives to the harvested raw material to produce silage raw material, if the determined amount of silage additive exceeds zero.

The analysis tool includes devices that provide the determination of parameters. In preferred embodiments, the analysis means may include a spectrometer, such as an infrared spectrometer (preferably a near infrared spectrometer), terahertz spectroscopy (preferably time-resolved terahertz spectroscopy), a Raman spectrometer, an ultraviolet spectrometer, a mass spectrometer, such as a time-of-flight MALDI spectrometer, nuclear magnetic spectroscopy, laser breakdown excitation spectrometer and others. In another embodiment, the analysis means may include at least one chromatograph, such as a gas chromatograph (GC), high performance liquid chromatograph (HPLC), or others. In other examples, the analysis means may include a thermal imaging device, a wet-path analysis device, and/or a trace element analysis device. One or more devices from each category can be combined to increase the amount of information on the collected raw materials. Preferred analysis tools include those that enable fast and reliable measurements and operational use in the field. Spectrometers, preferably infrared spectrometers and most preferably near infrared spectrometers, are particularly useful here.

In addition, the device may include a device for feeding, crushing, threshing, crushing, flattening, grinding or cleaning the collected raw materials, for example, an agricultural grinder for crushing the collected raw materials. Here, it is particularly preferred that the device is equipped with an analysis tool to enable analysis in the field, for example an agricultural chopper equipped with an analysis tool.

In addition, the device may include a means of transport, such as a conveyor belt or the like, for transporting the harvested raw material, optionally processed using the above feeder, crushing, threshing, crushing, flattening, grinding or cleaning the harvested raw material, after harvesting. to the analysis means and/or from the analysis means to the feeder and from the feeder to the silo.

In addition, the device may include leveling means for homogenizing or evenly distributing the collected raw material, optionally processed with the above device for feeding, crushing, threshing, crushing, flattening, grinding or cleaning, to form a uniform stream of (processed) collected raw material. Such a leveling means provides the possibility of improving or optimizing the provision of the collected raw material to the analysis means, for example, by leveling the surface of the stream of (processed) collected raw material. One example of such a leveling means is a roller in the form of an elongated shaft, which is preferably located at a constant and fixed distance along the axis of the roller above a conveyor belt that guides the (processed) collected raw materials to the analysis means. With this roller, the (treated) collected raw material can be compressed to a certain thickness, thus achieving a flat surface. In a preferred embodiment, the motor drives the roller and rotates it in the direction of travel of the conveyor belt, in a more preferred embodiment, the motion of the roller is provided by the drive of the conveyor belt. A block or other roller may be placed under the belt to counteract the pressure of the first roller. The block or other roller ensures that there is no downward pressure on the belt and thus the stream of (processed) harvested material is generally at a preselected height after the first roller has passed. In a second example for radiation-based analysis means, preferably infrared spectroscopy, more preferably near infrared spectroscopy, a stream of (processed) harvested feedstock is passed or guided along a window to transfer the appropriate radiation between the analysis tool and the (treated) harvested feedstock. Window material such as glass or plexiglass can be removed for cleaning or replacement. Cleaning or replacement of the window material may be required because residues from the process stream, such as processed harvested material passing through the window, may accumulate on the window material or damage the window material, interfering with system operation. When the (processed) collected raw materials pass along the window, the flow and the flow surface on the side of the window are leveled.

Additional embodiments of the present invention are defined in the dependent claims and in the present description. All embodiments of the invention can be combined with each other, unless otherwise indicated.

Brief description of the drawings

Here the invention will be described in more detail in terms of preferred embodiments of the invention with reference to these figures.

In FIG. 1 shows a flow diagram of a silo preparation method according to a preferred embodiment of the invention;

in fig. 2 is a flow diagram of a method for obtaining a first model for determining the ensilability index number used in the method of FIG. 1;

in fig. 3 is a flow diagram of a method for obtaining a second model for determining the retention index number used in the method of FIG. 1;

in fig. 4 is a graphical representation of the ensiling index number and the harvested raw material retention index number determined by the method of the present invention;

in fig. 5 is a schematic diagram of a silo preparation apparatus according to a preferred embodiment of the invention; and in FIG. 6 is a block diagram of an ensiling process according to a preferred embodiment of the present invention.

In FIG. 1 shows a flowchart of a silo preparation method 100 in accordance with a preferred embodiment of the invention.

The method starts at step S11 and continues at step S12, where the collected raw material is obtained. This stage includes the well-known processes of harvesting crops. Harvesting can be done by hand, but is usually done with agricultural harvesters. In addition to harvesting the raw material, process step S12 may, if desired, include mechanical processing of the harvested raw material, such as feeding, grinding, threshing, crushing, flaking, grinding or cleaning, to convert the harvested raw material into a size or shape suitable for ensiling and/or leveling/homogenization of (recycled) harvested raw materials. The harvested raw material may include any crop suitable for ensiling, in particular the previously mentioned crops such as corn. In the case of corn, the harvested raw material is the entire aboveground plant or just the corn kernels.

In the next step S13, the collected and, if required, mechanically processed/homogenized raw materials are subjected to an analysis process to determine a set of parameters xi characterizing the chemical composition and/or other property/characteristic of the collected raw materials. Here, x denotes the parameter resulting from the analysis, and index i denotes an index number from 2 to N, where N denotes the total number of parameters determined in the method. The parameter may include any component in the collected raw material, in particular the previously mentioned components. According to a specific example, in step S13, seven parameters xi can be determined, including dry matter content (DM), mass concentration of sugars (TS), mass lipid concentration (EE), content of acid detergent fiber (ADF), content of acid detergent lignin ( ADL), neutral detergent fiber content (NDF) and starch content (ST). The analysis process for determining the parameters xi can use any method capable of providing the information of interest, in particular the methods mentioned earlier. In accordance with a particularly preferred example, near infrared spectroscopy (NIR) is used in step S13, since it is able to determine all the parameters mentioned.

- 6 042275

The procedure then proceeds to step S14 where the first model Ini=f(x _i ) is applied to the parameters xi determined in step S13 to determine an ensilability index number Ini representative of the expected ensilability of the harvested raw material. The number Ini of the silage suitability index reflects the expected quality of the silage raw material after silage and can be a dimensionless number. Preferably, the silageability index number Ini is a value ranging from the worst quality of the silage raw material to the theoretical maximum quality. The first model can be stored in the form of a multidimensional lookup table assigning an ensilability index number Ini to the parameters xi. In another embodiment of the invention, the first model may be a mathematical equation expressing the number Ini of the ensiling index as a function of the parameters xi. An example of a mathematical equation obtained by regression analysis representing the first model is shown in the following Equation 1:

Here Ci and Bi are the first and second order regression coefficients for the ith parameter x, respectively. The first model was obtained in advance with the help of empirical studies. An example of obtaining the first model will be described later in FIG. 2. The result of step S14 is thus the ensilability index number Ini in the form of one characteristic value.

The procedure then proceeds to step S15, in which the second model Rei=f(xi) is applied to the parameters xi determined in step S13 to determine the Rei number of the retention index indicative of the expected dry matter loss or retention of the harvested raw material during ensiling. The retention index number Rei can be a dimensionless number or a percentage. Thus, it represents the relationship between the chemical composition of freshly harvested raw material (expressed as xi) and the expected dry matter content of the silage. For example, the Rei number of the retention index is an indicator ranging from the theoretical minimum dry matter loss to the theoretical maximum dry matter loss during the ensiling process. Similar to the first model, the second module can be stored in the form of a multidimensional lookup table assigning a retention index number Rei to the parameters xi. In another embodiment of the invention, the second model may be a mathematical equation expressing the number Rei of the retention index as a function of the parameters xi. An example of a mathematical equation obtained by regression analysis representing the second model is shown in the following Equation 2:

N N

Rei = Intercept +

Here Di and Ei are the first and second order regression coefficients for the i-th parameter x, respectively. The second model was obtained in advance with the help of empirical studies. An example of obtaining the second model will be described later in FIG. 3. The result of step S15 is thus the number Rei of the retention index in the form of one characteristic value.

After determining the ensiling index number Ini and the retention index number Rei, the method proceeds to step S16, where the type ai of the ensiling additive(s) and the amount m_ai of the corresponding additive added to the harvested ensiling process control raw material are determined based on the index number Ini suitability for ensiling and the Rei number of the retention index. Step S16 may include a conversion table in which the type and amount of silage additive are assigned to Ini and Rei. Alternatively, step S16 may include a mathematical function expressing the type ai and the additive amount m_ai as a function of Ini and Rei. According to a specific embodiment of the invention, in step S16, a decision can be made as to whether the number Ini exceeds the ensilability index of the first threshold value Ini ₁ . Depending on the excess or non-exceedance of Ini of the first threshold value Ini1, the type and content of the additive selected from the first aforementioned group of additives is determined. Since the Ini number of the ensilageability index indicates the ability of the chemical ingredients of the harvested raw material to be converted into high quality silage, the first group of additives preferably includes homofermentative microorganisms, sugars and/or water, in particular those mentioned previously. Similarly, in step S16, a decision can be made as to whether the Rei number exceeds the retention index of the second threshold Rei1. Depending on the excess or non-exceedance of Rei of the second threshold value Rei1, the type and content of the additive selected from the second aforementioned group of additives is determined. Since the Rei number of the retention index reflects the expected loss of dry matter during the ensiling process, the second group of additives preferably includes heterofermentative microorganisms, in particular those mentioned earlier. It is understood that if the indices Ini and Rei indicate that the harvested raw material is expected to be easily converted into high quality silage with low dry matter loss, the addition of any additives to the raw material may not be required. In this case, the amount of added additives is determined at the level of zero

- 7 042275 values.

For the purposes of step S16, the determined ensilability and retention indices Ini and Rei can be visualized in a 2D graphical representation as shown in FIG. 4 (harvested raw material=corn). Here, the Retention Index Rei is shown on the abscissa as a percentage of dry matter loss (based on the total dry matter of harvested raw materials) and the Ensilage Index Ini is shown on the y-axis. The first threshold value Ini1 for the ensiling index number corresponds in this example to a value of 54.4, while the second threshold value Rei1 corresponds to a dry matter loss of 7% by weight.

By means of these thresholds, the field is divided into four quarters (quarter 1 to quarter 4). Thus, the collected raw material belongs to one of the quarters 1 to 4 (for example, quarter 2, as shown in Fig. 4), which causes the occurrence of one of the following scenarios.

Quarter 1 (Ini>54.4; Rei<7%): corn has a balanced chemical composition that allows you to quickly activate the desired fermentation process. To stimulate fermentation, correction by adding silage additives is not required.

Quarter 2 (Ini>54.4; but Rei>7%): corn has a good chemical composition but is not able to effectively contain canning losses (possibly due to the high moisture content of freshly cut corn). To speed up and optimize the fermentation process, additives (hetero-fermentative and/or homo-fermentative microorganisms) should be used.

Quarter 3 (Rei<7%; but Ini<54.4): The chemistry is not able to effectively stimulate fermentation. If the dry matter content exceeds 45%, additives (hetero-fermentative and/or homo-fermentative microorganisms, water and sugar) must be used to achieve sufficient fermentation.

Quarter 4 (Ini<54.4; Rei>7%): good chemistry, but the established process is not ideal in terms of quality and dry matter retention, i.e. the chemical composition is not able to provide a sufficiently high suitability for fermentation due to inappropriate cob/raw ratio, fermentation processes are not ideal in terms of quality and dry matter retention. Additives should be used to improve the fermentation process.

Again with reference to FIG. 1 after defining types a; and the amount of ensiling additives m_a, the procedure proceeds to step S17, where the additives determined in step S16 are added to the raw material in appropriate amounts. The addition of additives may be carried out in any appropriate manner depending on the form of the additive. Preferably the additives are added in the form of solutions or suspensions by spraying or the like. In this way a homogeneous distribution or mixing of the additive(s) in the collected raw material is achieved.

The method ends at step S18 with silage, including harvested raw material plus additive(s), ready to be ensiled.

According to a preferred embodiment of the invention, method 100 is carried out continuously as a harvesting process. Thus, the parameters xi can be determined continuously in a step-by-step manner for the freshly cut fractions of the harvested raw material. This makes it possible to continuously add additives to the respective fractions of the raw material, depending on the composition of the respective fraction, and to prepare different fractions that differ in the type and/or amount of added silage additive. In this way, different silage fractions can be fed into the silo.

Then, the methods for obtaining the first and second models used in the method of FIG. 1 will be described in FIG. 2 and 3, respectively. It is contemplated that these methods will only be produced once for permanent use in the method of FIG. 1.

In FIG. 2 describes a method 200 for obtaining a first model for determining ensilability index number Ini as a function of parameters xi.

The method starts at step S21 and proceeds to step S22, where the test samples of the harvested raw material of the crop of interest are collected. Preferably samples are collected from different varieties, different climates and different harvest periods to ensure wide distribution of the compositions. In a specific example, the studied corn samples were collected from 29 varieties (including early and late varieties), from three production sites (different in soil and climatic conditions) and during the period of early, normal or late harvest. After harvesting, all samples were crushed.

The method then proceeds to step S23 where the test samples are analyzed to determine experimental parameters x _ije characterizing the chemical composition and/or properties of the collected test samples. Here, the experimental parameters x _ije and the analysis methods may be similar to those described for step S13 in FIG. 1.

The method then proceeds to step S24 where the test samples are processed into silage by ensiling using predetermined standard conditions. Preferably, the predetermined conditions are favorable conditions under which an op is expected.

- 8 042275 thymized or optimal fermentation. The ensiling process can be carried out in large silos in the field or in the laboratory. In a specific example, the ensiling process was carried out under the following laboratory conditions: two samples (500±50 g) for each freshly harvested whole ground raw corn were placed in sealed bags (Orved 2633040, Orved SpA, Musile di Piave, VE, Italy). Bags (300x400 mm) with a thickness of 90 microns are made of polyamide and polyethylene (PA / PE) with a gas permeability coefficient at 23 ° C ± 2 of 65, 15 and 200 cm ³ m - ² days ^{- 1} atm - ¹ for oxygen, nitrogen and CO ₂ respectively. Packing in sealed containers was carried out using a vacuum packaging machine (Cuisson 41, Orved SpA, Musile di Piave, VE, Italy) with a throughput of 25 m ³ air per hour for 12 seconds. Then, after removing the air, the bags were automatically packed. Samples were processed and stored at 23° C. for 60 days before opening the bags for analysis to avoid swelling.

At step S25, the silo from the test samples is again analyzed to determine the second experimental parameters yi, _e characteristic of the chemical composition and/or other properties of the silo. The parameters determined here can be selected based on the pH value, lactic acid content, acetic acid content, butyric acid content, ethanol content, mannitol content, ammonia content. In addition, the similar parameters previously mentioned with respect to the parameters xi can be determined in step S25. Further, at this stage, the previously described methods of analysis can be applied. In a specific example, the contents of each bag were analyzed in duplicate using BIS spectroscopy to determine the content of dry matter (DM), crude protein (CP), ash (AS), starch (STA), ethereal extract (EE), neutral detergent fibers (NDF) and acid detergent fibers (ADF) using a calibration curve. The content of lactate, volatile fatty acids (VFA), ethanol and mannitol was determined using HPLC. Ammonia was measured using a reagent kit (Megazyme). Next, the pH level was measured. Then the mean values for each parameter were calculated.

In the subsequent step S26, each silo sample under investigation is assigned a quality index QI depending on the parameters yi, _e . Here, the quality index QI known in the art can be used to evaluate the quality of the silage. In a specific example, one of the quality indices I1 to I6 described by Igino Andrighetto et al. (Andrighetto et al., supra) was used. As shown in Table. 1, indexes I1 to I6 are based on the content of lactic acid, ammonia, ethanol, acetic acid, butyric acid, mannitol and pH value. For each parameter yi, _e , a range of values was predetermined to correlate with the content range (based on the DM dry matter value). For example, the minimum lactic acid content (22.6 g/kg dm) would be 0 and the maximum lactic acid (60.0 g/kg dm) would be 41, and so on. The maximum quality index is 100 for each of the indexes I1 to I6.

Table 1

Parameters Value range (g/kg DM) Indicator interval 16 eleven 12 13 14 15 Dry matter (g/kg) 390 - 276 - - - - - 0-6 Neutral Detergent Fiber (NDF) 518-410 - - - - - 0-39 Acid Detergent Fiber (ADF) 297 - 220 - - - - - 0-2 Lactic acid 22.6 - 60.0 0-41 0-41 0-41 0-41 0-41 0-17 Ammonia 82.0 - 35.4 0-18 0-18 0-18 0-18 0-18 - ethanol 8.70-2.10 0-18 0-18 0-18 0 - 18 0 - 18 - Acetic acid 27.4 - 7.20 0-9 0-9 0-9 0-9 0-9 0-20 pH' 3.97-3.65 0-8 0-4 0-4 0-6 - - Butyric acid 1.00 - 0.00 - 0-10 0-7 0-2 0-14 - Mannitol 12.7 - 0.90 0-6 - 0-3 0-6 - 0-16 Maximum Index - 100 100 100 100 100 100

^a Expressed as a dimensionless number.

Accordingly, in step S26, the quality index QIi is obtained for each silo depending on its chemical composition.

The method then proceeds to step S27 where the quality scores QIi are correlated with the parameters xi, _e determined before ensiling in step S23. The correlation step may include a mathematical regression analysis that will result in a mathematical function (including regression parameters) expressing the ensiling index number Ini as a function of the parameters xi, _e . In the present example, the inventors used data from all analyzed maize plants

- 9 042275 without or after transformation (logarithmic, squared, etc.) or combinations thereof. In addition, many regressions were calculated using the SAS software and differed in the parameters considered (analysis) and/or the transformation applied. Finally, the regression with the higher R-squared value was adopted, giving the best correlation (see Equation 1 above). In table. 2 shows the regression parameters of the regression function obtained for the studied corn samples.

table 2

First model regression coefficients to predict suitability for ensiling (values calculated from % dry matter)

Xi parameters. _e Selection Ci Bi 197.11574 Dry matter (DM) -6.83667 0.07552 Mass concentration of sugars (TS) 0.27189 0 Lipids (EE) 11.37159 0 ADF 1.68108 0 Acid Detergent Lignin (ADL) 12.55362 0 NDF 0 -0.01528 Starch (STA) 0 -0.00939

Thus, the regression function Im=f(x _i>e ) according to the first model using the regression coefficients from Table. 2 can be expressed as follows (equation 1.1):

Ini = 197.11574 - 6.83667DM + 0.27189TS + 11.37159EE + 1.6810ADF 12.55362ADL + 0.07552DM ² - 0.01528NDF ² - 0.00939STA ²

Therefore, the procedure in Fig. 2 ends at step S28, thus providing a first model Ini=f(xi, _e ), which can then be applied in the procedure of FIG. 1 to determine the expected ensilability of freshly harvested corn.

In FIG. 3 describes a method 300 for obtaining a second model for determining the retention index number Rei as a function of parameters x _i .

Here, steps S31 to S34 correspond to steps S21 to S24 in FIG. 2 and are not described again. In fact, both methods of determining the first model for ensiling index and determining the second model for retention index can be performed in one combined process with separation of the respective steps.

After the ensiling process is completed in step S34, the routine proceeds to step S35, in which the silage of test samples is subjected to an analysis process to determine the DM dry matter content of each test sample.

The method then proceeds to step S37 where the dry matter content DM in the silage samples under investigation is correlated with the parameters xi, _e determined in step S33 before ensiling. The correlation step may include a mathematical regression analysis, which will result in a mathematical function (including regression parameters) expressing the Rei number of the retention index as a function of the parameters xi, _e . In the present example, the inventors used the same as described with respect to FIG. 2 approach for accepting a regression with a higher R-squared value (see Equation 2 above). In table. 2 shows the regression parameters of the regression function of the second model obtained for the studied corn samples.

Table 3

Second model regression coefficient for predicting dry matter loss (values calculated from % dry matter) assuming I1 index as quality index QI as reference

Parameters x^ _e Selection Di Ei 223.34276 Dry matter (DM) -5.10303 0.06241 Mass concentration of sugars (TS) -2.60745 0.13445 Acid Detergent Lignin (ADL) -9.80569 0 Starch (STA) -2.62779 0.02439 NDF 0 0.00953 ADF 0 -0.0891

- 10 042275

Thus, the regression function Rei=f(xi,e) according to the second model using the regression coefficients from Table. 3 can be expressed as follows (equation 2.1):

Rei = 100 - (223.34276 - 5.10303DM - 2.60745TS - 9.80569ADL - 2.62779STA +

0.06241DM ² + 0.00953NDF ² - 0.0891ADF ² + 0.02439STA ² + 0.13445TS ² )

Therefore, the procedure shown in Fig. 3 is completed at step S38, thus providing a second model Rei=f(xi,e) which can then be applied in the procedure of FIG. 1 to determine the expected dry matter loss of freshly harvested corn during the ensiling process.

Further in FIG. 5 describes an apparatus for preparing improved quality silage for ensiling.

The device 10 includes an agricultural grinder 11 for mechanical processing of freshly harvested raw materials 20 to obtain particles of a suitable size for ensiling. The device further comprises a conveyor belt 12 for transporting the collected raw materials to various processing stations of the device 10. Thus, the crushed collected raw materials 20 are conveyed by a conveyor belt from the grinder 11 to the analysis means 13 for analyzing the freshly harvested and crushed raw materials 20 to determine a set of parameters xi characterizing the chemical composition , and/or other properties of the collected raw materials, as described above. Here, the analysis means 13 comprises a near infrared spectrometer NIRS. The NIRS emits near-infrared radiation of selected wavelengths onto the ground collected raw material 20 and determines the corresponding absorption or transmission. The device 10 further comprises a computing station 14 connected to an analysis means 13 for analyzing the near infrared data provided from the NIRS spectrometer, for example, to determine the parameters xi from the near infrared data. Computing station 14 further performs steps S14 through S16 (FIG. 1). For this purpose, the computing station 14 includes first and second models for determining the ensilability index number Ini and the retention index number Rei as a function of the parameters xi. For example, the models may be stored in the form of equations Equation 1.1 and Equation 2.1 as shown above in a machine readable format. In addition, the computing station 14 includes an algorithm for determining the types ai and amounts m_ai of silage additives to be added to the harvested raw material 20 depending on the ensilability index number Ini and the retention index number Rei, as described above. If desired, the device 10 may include a display means 15 for displaying the result of the analysis, for example, the determined indices Ini and Rei in the form of a 2-dimensional representation shown in FIG. 4. The apparatus 10 further comprises a supply means 16, such as a spray means, for supplying ensilage additives 17 to the harvested raw material 20 in accordance with the types ai and quantities m_ai previously determined. For this purpose, the collected raw material 20 is further conveyed by a conveyor belt 12 to a supply means 16, where ensilage additive, in particular microbial inoculum, is sprayed onto the collected raw material, whereby a silo 30 is formed. 18, in which the subsequent ensiling process will take place. As shown in FIG. 5, the silo 18 is thus filled with different layers of silage 30 containing various additives (if any) selected here from two mixtures of inoculum selected by computer station 14 depending on the number Ini of the silageability index and the number Rei of the retention index.

Next, the ensiling process 400 according to the present invention is explained with reference to FIG. 6. The process starts at step S41 and proceeds to step S42, where the preparation of the silo 30 in accordance with the invention is carried out. In other words, step S42 comprises steps S11 to S18 of the method 100 described with reference to FIG. 1. After preparing the silage 30, the method proceeds to step S43, in which the silo is subjected to fermentation, i. e. ensiling, to obtain the desired ensiling product. Ensiling involves well-known microbiological fermentation processes. However, by preparing silage according to the present invention, including the addition of additives selected based on the composition of the harvested raw material, the ensiling process is improved compared to a process in which additives are not added or in which additives are added in a rather intuitive manner. The result is an improved quality silage product containing more animal feed nutrients and dry matter.

Reference positions

- Silo preparation device;

- agricultural grinder;

- means of movement, conveyor belt;

- analysis tool, NIRS;

- means of determination, computing station;

- means of display;

- means of supply;

- additive for ensiling;

- 11 042275

- silo bunker;

- collected raw materials;

- silo;

100 - a method of preparing a silo;

200 - the method of obtaining the first model used in the method 100;

300 shows a method for obtaining a second model used in method 100;

400 - ensiling process;

Ini - index number of suitability for ensiling;

Rei is the retention index number;

xi - parameters characterizing the chemical composition and/or properties of the collected raw materials;

xi, _e - parameters characterizing the chemical composition and/or properties of the collected test sample before ensiling;

yi, _e - parameters characterizing the chemical composition and/or properties of the test sample after ensiling;

ai - type of additive for ensiling;

m_ai - amount of additive for ensiling.

Claims (15)

CLAIM 1. Method (100) for preparing a silo (30) with improved quality characteristics for the ensiling process, including the process of analyzing freshly harvested raw materials subjected to the ensiling process to determine a set of parameters (xi) characterizing the chemical composition and/or properties of the collected raw materials (20); applying the first model to certain parameters xi to determine an ensilability index number Ini that is indicative of the expected ensilability of the harvested raw material; applying the second model to certain parameters (xi) to determine a retention index number (Rei) that is indicative of the expected dry matter (DM) loss of the harvested raw material (20) during the ensiling process; determining the types and amounts of ensiling additives (17) to be added to the harvested raw material (20) in order to control the ensiling process depending on the number (Ini) of the ensiling index and the number (Rei) of the retention index; and adding certain additives (17) to the harvested raw material (20) in order to produce silage (30), in case the certain amount of silage additive (17) exceeds zero. 2. The method according to claim 1, in which the analysis process includes a spectroscopic method, such as infrared spectroscopy, preferably near infrared spectroscopy; terahertz spectroscopy, preferably time-resolved terahertz spectroscopy, Raman spectroscopy, ultraviolet spectroscopy, mass spectroscopy, such as time-of-flight MALDI spectroscopy, nuclear magnetic spectroscopy, laser breakdown excitation spectroscopy, and/or a chromatographic method such as gas chromatography and high performance liquid chromatography ; thermal imaging techniques, wet-path analysis, and trace element analysis. 3. Method according to any one of claims 1 and 2, wherein the harvested raw material (20) is selected from corn, in particular Zea mays, sorghum, in particular S. bicolor, sugar cane, in particular Saccharum officinarum, rye, in particular Secale cereale , triticale, barley, in particular Hordeum vulgare, wheat, in particular Triticum aestivum, perennial chaff, for example Lolium perenne, and alfalfa, in particular Medicago sativa. 4. The method according to any one of claims 1 to 3, wherein the parameters (xi) are selected by pH value, water content, dry matter content (DM), ash content (A), ash insoluble in acid (AIA), crude protein content (CP), sugar content (TS), glucose content (GLU), fructose content (FRU), sucrose content (SAC), ether extract content (EE), starch content, cellulose content, lignin content, crude fiber content (CF), acid detergent fiber (ADF) content, neutral detergent fiber (NDF) content, and acid detergent lignin (ADL) content. 5. The method according to any one of claims 1 to 4, wherein the first model used in the method is obtained by correlating the parameters (xi, _e ) measured on test samples before ensiling with the chemical composition of the obtained silage after ensiling using predetermined conditions. 6. The method according to any one of claims 1 to 5, wherein the second model used in the method is obtained by correlating the parameters (xi, _e ) measured on test samples before ensiling with the dry matter content after ensiling under predetermined conditions. 7. The method according to any one of claims 1 to 6, wherein the first model and/or the second model used in the method are obtained using mathematical regression analysis. 8. The method according to any one of claims 1 to 7, wherein determining the types and amounts of silage additives (17) added to the collected raw material (20) includes 9. The method according to claim 8, in which the first and second groups of additives include inoculums of various bacterial strains, in particular Lactobacillus Plantarum, Lactobacillus sasei, Streptococcus faecium, Pediococcus pentosaceus, Lactobacillus curbatus, Lactobacillus coryniformis ssp. coryniformis, Pediococcus acidilactici, Lactobacillus brevis, Lactobacillus buchneri, Lactobacillus fermentum, Lactobacillus viridescens, Lactobacillus rhamnosus, Leuconstoc cremoris and Leuconstoc dextranicum. 10. The method according to claim 9, in which the first group of additives includes Lactobacillus Plantarum, Lactobacillus sasei, Streptococcus faecium, Pediococcus pentosaceus, Lactobacillus curbatus, Lactobacillus coryniformis ssp. coryniformis, Pediococcus acidilactici; and the second group of additives includes Lactobacillus brevis, Lactobacillus buchneri, Lactobacillus fermentum, Lactobacillus viridescens, Lactobacillus rhamnosus, Leuconstoc cremoris and Leuconstoc dextranicum. 11. The method according to any one of claims 1 to 10, wherein the method is carried out continuously during the collection of the raw material (20). 12. The process (400) ensiling, including the preparation of the silo (30) by the method according to any one of paragraphs.1-11; and fermenting the silage (30) to obtain an ensiled product. - 12 042275 comparison of the number (Ini) of the ensiling index with the first threshold value (Ini1): depending on whether a certain ensiling index number (Ini) of the first threshold value (Ini1) is exceeded or not exceeded, the type and content of the additive selected from the first group of additives; and comparing the number (Rei) of the retention index with the second threshold value (Rei1): depending on the excess of a certain number (Rei) of the retention index of the first threshold value (Rei1) or no excess, the type and content of the additive selected from the first group of additives is determined. 13. Device (10) for preparing silage (30) of improved quality for ensiling, the device includes an analysis tool (13) for analyzing freshly harvested raw materials (20) to determine a set of parameters (Xi) characterizing the chemical composition and / or property of the harvested raw materials (20); applying the first model to the estimated parameters (xi) to determine an ensiling index number (Ini) that is indicative of the expected ensiling suitability of the harvested raw material (20), where said first model is represented by the equation 1 N N where Ci and Bi are regression coefficients first and second order for the i-th pair B^ ² (1)< meters x, respectively; application of the second model to the estimated parameters (xi) to determine the number (Ref) of the retention index, which is representative of the expected loss of dry matter of the harvested raw materials (20) during the ensiling process, where the specified second model is represented by equation 2 Rei = Intercept + Di %i + Σί=ι x^ ² (2), where Di and Ei are the first and second order regression coefficients for the i-th parameter x, respectively; determination of means (14, 15) for determining the types and amounts (ai, m_ai) of additives (17) for the ensiling process, which are added to the harvested raw material (20) in order to control the ensiling process depending on the number (Ini) of the ensiling suitability index and numbers (Rei) of the retention index; and supply means (16) for adding certain additives (17) to the harvested raw material (20) in order to produce silage (30), in case the certain amount (m_ai) of the silage additive (17) exceeds zero. 14. The device (10) according to claim 13, further comprising a device (11) for feeding, grinding, threshing, crushing, flattening, grinding or cleaning the collected raw materials (20), while the device (11) is equipped with an analysis means (13). 15. The device (10) according to any one of claims 13 and 14, additionally containing means (12) for transporting the collected raw materials (20) from the means (13) of analysis to the means (16) of supply and from the means (16) of supply to the silo bunker (18).