Improvements in and related to agricultural land use

Abstract

A method of providing precise nutrient management in terms of what agricultural interventions are or are not required for obtaining crops / pasture for feeding grazing animals for a future period following said agricultural intervention being deployed, the method comprising the following steps: selecting a geographic area of interest (GAOI); obtaining pasture production map with categorized or graduated mapped sections for pasture production across the GAOI; assessing amount of pasture able to be eaten by a typical stock unit by adjusting pasture production map from step b) to take into account stock utilization for each mapped section within the GAOI; calculating – from said information from step c) – number of stock required to consume feed, taking into account stock utilisation to produce a nutrient / agrochemical application map that replaces nutrient otherwise exported / removed / lost from the GAOI, applying fertiliser as per the nutrient / agrochemical map.

Description

[0001] James & Wells ref: 325511

[0002] IMPROVEMENTS IN AND RELATED TO AGRICULTURAL LAND USE

[0003] STATEMENT OF CORRESPONDING APPLICATIONS

[0004] This application is based on the Provisional specification filed in relation to New Zealand Patent Application Number 818961, and Complete specification filed in relation to Australian Patent Application Number 2025201202 the entire contents of which are incorporated herein by reference.

[0005] TECHNICAL FIELD

[0006] This invention relates to the improvements in and related to agricultural land use. Specifically, this invention relates to an improved way of best utilising farmland by more accurately calculating the correct amount of fertiliser (or other agrochemicals) to achieve a desired agronomic outcome, both in terms of pasture and / or cropping required and / or livestock production levels, to provide sufficient feed for the desired stocking units and their expected production to be sustained on a specified area of land.

[0007] BACKGROUND ART

[0008] At present environmental problems exist in agriculture, with either: over fertilising, and / or over applying other agrochemicals, to an area of land. These problems include but should not be limited to:

[0009] greenhouse gas emissions;

[0010] nitrate leaching;

[0011] particulate phosphorus (P) loss;

[0012] over fertilisation;

[0013] poor land utility.

[0014] overstocking and soil compaction

[0015] A related problem that also exists is the underutilisation of an area of land by farmers, farm managers or agricultural consultants, - (now for ease of reference now all simply referred to as farmers) - who do not fully understand how best to use said area of land to deliver needed agricultural benefits to not only feed humanity but also preserve the environment - (i.e. making best optimisation of our limited resources which include land, water and agrochemicals).James & Wells ref: 325511

[0016] So, the above problems, as well as being wasteful / harmful, also have an economic impact on farmers who are spending more money than they should, or also perhaps, not enough money in certain cases. All due to a lack of understanding of how land is utilised and the constraints of an area of land in terms of:

[0017] - supporting stocking units for a given faming application, or

[0018] - growing pasture / crops for a given farming application.

[0019] It would be useful if there could be provided ways to not only reduce waste and harm to the environment as well as minimising overspending by farmers, but also provide quick and accurate ways to calculate how best to utilise an area of land in terms of maximum number of stocking units that can be supported on said land, and how to maintain the feed supply, on that area of land in order to do so.

[0020] Currently, all estimates for the future amount of fertiliser or other agrochemical to be applied to an area of land requires, among other things, answers for one or more of the following:

[0021] - the farmer’s input in terms of number of stocking units for a given type of livestock animal they currently are successfully sustaining, or they are currently trying to unsuccessfully sustain, or in future maximum numbers they want to sustain on said area of land;

[0022] - the famer’s input in terms of desired milk production per hectare (kg MS / ha);

[0023] - the farmers input in terms of annual feed intake by the grazing animals

[0024] - the famer’s input in terms of live weight per hectare (kg / Ha).

[0025] This further information from farmers however is generally - given subjectivity - imperfect and can thus lead to, in practice, to either:

[0026] an over utilisation, or

[0027] underutilisation;

[0028] of said area of land (or a portion thereof).

[0029] As such, some of the technical problems to be solved by the present invention are:

[0030] How to determine the amount of fertiliser or other agrochemicals required to be applied to a specific area of land (i.e. geographic area of interest (GAOI) or a geographically distinct subunit thereof) from recent and historical data in a pasture layer for said GAOIJames & Wells ref: 325511

[0031] in order to ensure there is sufficient feed produced by said GAOI to support a desired number, and / or maximum number, of stocking units over a set time period;

[0032] How to determine - what is the maximum number of stocking units a GAOI and / or localised mapped section - can support, from recent and historical data in a pasture layer for said GAOI.

[0033] A further technical problem that is solved by the present invention is how to determine the required amount, and not an overabundance, of, any one or more of the following:

[0034] - fertiliser;

[0035] agrochemicals;

[0036] to maximise the stocking units for a given type of animal to be sustained over a 365 day or other period, by requiring no more than the provision of geographic information detailing geographic coordinates, or a shapefile (or the like), from the farmer, which defines the GAOI upon which the livestock will be reared / grazed.

[0037] The present invention has the objective of not only providing an improved user interface which looks to not only show high pasture or crop production mapped sections within a GAOI but also assesses the utility of these mapped sections for supporting livestock / crops / pasture taking into account the terrain, and usage of the terrain, or surrounding terrain in hill country - which may affect said intended uses.

[0038] A further problem to be solved is a method of how to provide a way that anyone:

[0039] - with, or without any, previous farming experience of an area of land, on which they wish to farm (i.e., the GAOI); -

[0040] can be provided with the required amounts of:

[0041] - fertiliser;

[0042] agrochemicals;

[0043] to farm the land (over the GAOI) so as to maximise the agronomic benefits of the land in terms of livestock or crops / pasture without:

[0044] an adverse economic impact to the famer due to oversupply or undersupply of fertiliser, agrochemicals; orJames & Wells ref: 325511

[0045] adverse environmental impact; or

[0046] - wasteful impact in terms of overuse of limited resources for increasing production yield;

[0047] merely by the farmer supplying details defining the geographic coordinates of the GAOL

[0048] It would be useful to offer a 365 day continuous grazing solution to counter the need to move livestock multiple times over a 365 day period via a conventional rotational grazing protocol.

[0049] It would also be useful to offer a way to assess and detail how to supply annual nutrient requirements to a GAOI and in particular to tailor the supply of nutrients to mapped sections within the GAOI.

[0050] The present invention thus provides a computer implemented methodology to produce an artificial state of affairs in the physical world, which is enabling 365 day continuous grazing on an area of land (GAOI) yielding a useful result in the form of responsible farming techniques that can supply enough food to feed a growing population whilst reducing:

[0051] - the environmental problems associated with over application of nutrients; and reducing costs to the famer.

[0052] It is therefore an object of the present invention to address the foregoing problems or at least to provide the public with a useful choice.

[0053] All references, including any patents or patent applications cited in this specification are hereby incorporated by reference. No admission is made that any reference constitutes prior art. The discussion of the references states what their authors assert, and the applicants reserve the right to challenge the accuracy and pertinency of the cited documents. It will be clearly understood that, although a number of prior art publications are referred to herein, this reference does not constitute an admission that any of these documents form part of the common general knowledge in the art, in New Zealand or in any other country.

[0054] Throughout this specification, the word "comprise", or variations thereof such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element, integer or step, or group of elements integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.

[0055] Further aspects and advantages of the present invention will become apparent from the ensuing description which is given by way of example only.James & Wells ref: 325511

[0056] DEFINITIONS

[0057] The term ‘continuous grazing’ as used herein refers to allowing domestic livestock to graze a specific area, such as a GAOI, throughout a 365 day period by the customized supply of nutrients as required to support such grazing.

[0058] The term ‘rotational grazing’ as used herein refers to grazing two or more pastures or parts of a range in regular order, with definite recovery periods between grazing periods. Where only two fields are involved, sometimes called alternate grazing. Contrasts with continuous grazing.

[0059] The term ‘GAOI’ as used herein defines an area on a map and refers to a specific geographic area of interest definable by a set of geographic coordinates which is, or which will be, put to a specified farming application, and for which nutrient management is an important consideration.

[0060] The term ‘mapped section(s)’ as used herein refers to geographically defined subunit(s) of land definable by a set of geographic coordinates which forms a part of the GAOI.

[0061] The term ‘stocking unit(s)’ as used herein refers to a relative amount of dry matter that a notional animal may eat per year which can be used to help determine feed amounts no matter what animal is being fed on a GAOI or mapped section. For non-limiting examples, one stocking unit is equal to a feed allocation of 550 kgDM / SU / yr) (This is the amount of feed consumed by a 55kg ewe and her single lamb up until weaning) and therefore a jersey dairy cow may be equivalent of 8.4 SU / yr or (4620kgDM / yr).

[0062] The term ‘stock utilisation’ or ‘pasture utilisation’ as used herein refers to an animal specific factor representing the amount of available crop / pasture that can be actually consumed by a particular grazing animal taking into account the GAOI or mapped section therein.

[0063] The term ‘flat country’ or ‘plains’ as used herein refers to land / terrain with a minimal elevation change and gentle slopes of (0-7 degree gradients).

[0064] The term ‘rolling country’ refers to land / terrain with moderate elevation changes and gentle to moderate slopes of (8-15 degree gradients).

[0065] The term ‘hill country’ as used herein refers to land / terrain with steeper more pronounced elevation changes including Easy (16-25 degree) and Steep (greater than 26 degree or more gradients).James & Wells ref: 325511

[0066] SUMMARY OF THE INVENTION

[0067] According to a first aspect of the present invention there is provided a method of providing precise nutrient management over a set 12 month period on a non-rotational basis in terms of what agricultural interventions are, or are not, required for obtaining pasture / crops for feeding grazing animals, over a future 12 month period following said agricultural intervention being deployed, the method comprising the following steps:

[0068] a) Selecting a geographic area of interest (GAOI);

[0069] b) Obtaining pasture production map with categorised or graduated mapped sections for pasture production across the GAOI,

[0070] c) Assessing amount of pasture able to be eaten by a typical stock unit over said set 12- month period by adjusting pasture production map from step b) to take into account stock utilization for each mapped section within the GAOI;

[0071] d) Calculating - from said information from step c) - number of stock required to consume pasture, taking into account stock utilisation to produce a nutrient / agrochemical application map that replaces nutrient otherwise exported / removed / lost from the GAOI, that maintain sufficient pasture for the stock numbers over said 12-month period on a non-rotational basis;

[0072] e) Obtaining soil nutrient testing data inside and / or outside of GAOI, and / or recorded or remote sensed data related to soil or environment for mapped sections forming GAOI; f) Using the data from step e) to create one or more nutrient surface maps for nutrients of interest taking into account the mapped sections within the GAOI;

[0073] g) Using the soil nutrient testing data and / or the remote sensed data related to soil data from e) and augmented with data from f) with knowledge of optimal levels of nutrient for crop / pasture growth, to create optimised GAOI nutrient application maps; wherein said stock units are maintained on the GAOI over said set 12-month period without the need to rotate the stock units; and

[0074] h) Extracting from said nutrient maps what nutrients and the amount of said nutrients that need to be applied to said GAOI or mapped sections therein.

[0075] According to a further aspect of the present invention there is provided a method substantially as detailed above wherein the method includes the further step of:James & Wells ref: 325511

[0076] i) applying nutrients in amounts determined from previous step g) to the mapped sections within the GAOL

[0077] According to a still further aspect of the present invention there is provided a method substantially as detailed above wherein the nutrients applied at step i) include P, and K.

[0078] According to a still further aspect of the present invention there is provided a method substantially as detailed above wherein the nutrients applied at step i) does not include a nitrogen based nutrient but includes P and K.

[0079] Alternatively, to a famer inputting a GAOI or paddock this input may be instead created by a farm consultant or GPS-mapping company, -i.e., without any input from the actual farmers.

[0080] Moreover, a GAOI may be selected from a saved farm name or customer ID and navigating to the GAOI by farm name or customer ID, or dragging the mouse to make a selection of the GAOI (i.e. a farm or part of a farm) on map application like Google Earth.

[0081] According to a still further aspect of the present invention, there is provided a variable rate optimised GAOI nutrient map which is based on a maintenance application map which details levels of nutrient from crop / pasture growth for mapped sections within the GAOI wherein the map obtains details of nutrients required that are assessed from categorized pasture production maps, assessing amount of pasture that can be eaten by a typical stock unit, assessing stock utilization and taking into account soil testing data.

[0082] BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Further aspects of the present invention will become apparent from the ensuing description which is given by way of example only and with reference to the accompanying drawings in which:

[0084] Figure 1 A shows a GAOI for a dairy farm which has been selected from the HawkEye online map database.

[0085] Figure 1 B shows the dairy farm GAOI of Figure 1 A adjusted to show mapped sections within the dairy farm GAOI which form the effluent discharge area. This effluent discharge area should be subtracted from the maintenance nutrient requirement for the effluent GAOI.James & Wells ref: 325511

[0086] Figure 2A shows a pasture production layer for the dairy farm GAOI of Figure 1 wherein the mapped subunits have been categorized by colour coding.

[0087] Figure 2B shows a stock pasture utilisation map across the dairy farm GAOI of Figure 1.

[0088] Figure 3 shows the pasture available to be eaten for the dairy farm GAOI of Figure 1, after factoring in a utilisation factor.

[0089] Figure 4 shows a nutrient / agrochemical map that maintains nutrient levels over 365 days for the dairy farm GAOI for the number of stock units that can be supported by the dairy farm GAOI.

[0090] Figure 5 shows a maintenance P map wherein kg P / ha indicates the P required

[0091] to maintain the soil nutrient P concentrations over 365 days at the current stocking rate for the dairy farm GAOI.

[0092] Figure 6A shows locations where physical soil samples were taken as part of a zonal sampling strategy for the dairy farm GAOI of Figure 1.

[0093] Figure 6B shows locations where physical soil samples were taken as part of a paddock sampling strategy as per Figure 6A.

[0094] Figure 6C shows locations where physical soil samples were taken as part of a grid sampling strategy as per Figure 6A.

[0095] Figure 7 shows an Olsen P (Phosphorus) nutrient surface derived from the physical soil samples obtained and analysed from Figures 6A-6C above.

[0096] Figure 8 shows a QTK (Potassium) nutrient surface derived from the physical soil samples obtained and analysed from Figures 6A-6C above.

[0097] Figure 9 shows a pH surface derived from the physical soil samples obtained and analysed from Figures 6A-6C above.

[0098] Figure 10 shows a nutrient application map across the dairy farm GAOI.

[0099] Figure 11 shows a product application map across the dairy farm GAOI.

[0100] Figure 12 shows example of a livestock hill country GAOI.James & Wells ref: 325511

[0101] Figure 13 shows the pasture production layer across a livestock hill country GAOI.

[0102] Figure 14 shows a slope class map across a livestock hill country GAOI.

[0103] Figure 15 shows a stock pasture utilisation map across a livestock hill country GAOI.

[0104] Figure 16 shows pasture available to be eaten across a livestock hill country GAOI.

[0105] Figure 17 shows stock units per hectare across a livestock hill country GAOI.

[0106] Figure 18 shows a maintenance P map across a livestock Hill Country GAOI.

[0107] Figure 19 shows examples of soil testing from physical in situ samples across a livestock hill country GAOI.

[0108] Figure 20 shows an Olsen P (Phosphorus) nutrient surface for a livestock hill country GAOI.

[0109] Figure 21 shows a pH surface across a livestock hill country GAOI.

[0110] Figure 22 shows a nutrient application map across a livestock hill country GAOI.

[0111] Figure 23 shows a product application map across a livestock hill country GAOI.

[0112] Figure 24 shows a subsection of the product application map that matches the ability of the aircraft to apply fertiliser / agrochemical at the correct rate and placement across a livestock hill country GAOI.

[0113] Figure 25 shows the travel lines the aircraft makes on each flight path across a livestock hill country GAOI.

[0114] Figure 26 shows the proof of release map showing how well the aircraft delivered the prescribed rate from the nutrient application map across a livestock hill country GAOI.

[0115] Figure 27 shows the proof of placement map for a dairy farm GAOI.James & Wells ref: 325511

[0116] BEST MODES FOR CARRYING OUT THE INVENTION

[0117] Example 1 - Dairy farm - topography flat / rolling country

[0118] The method steps a) to g) of the present invention substantially as described above in relation to a dairy farm example are outlined below:

[0119] a) Selecting a geographic area of interest (GAOI).

[0120] • The GAOI in the form of a farm 1 is shown in Figure 1 A and is obtained from a polygon vector file (in this case Shapefile) which has been selected, from www.hawkeye.farm a publicly accessible website map database which has a large repository of farm boundary files. To further refine this map, or create a new map, a user can upload a polygon vector file to the Hawkeye database or create one by manually digitising a boundary around each field in a GAOI on a farm found in the Hawkeye database. The map in Figure 1 A was selected by requesting the farm 1 in the database after it had been digitised or uploaded.

[0121] • Adjusting, the above shapefile within the GAOI as required to show the effluent discharge area (i.e., via creating or importing as described above) in Figure 1B. This adjustment may be achieved by selecting mapped sections within GAOI or creating another new shapefile for the effluent discharge area, or if known the effluent discharge area shapefile can simply be uploaded.

[0122] • The shapefile currently uses a New Zealand Transverse Mercator (NZTM) projection which flattens the spherical shape into a flat map surface and allows metric units of measure for more information refer the following link:

[0123] New Zealand Transverse Mercator 2000 (NZTM2000) I Geodetic Guidance.

[0124] • This process for selection and refinement of the GAOI is the start of an automated chain to produce a maintenance nutrient plan or optimised nutrient plan for the GAOI requiring no further user input.

[0125] b) Obtaining pasture productivity layers

[0126] In New Zealand this can be achieved by accessing the following Landcare website which has a repository of information for pasture productivity throughout New Zealand (https: / / lris.scinfo.org.nz / layer / 105112-north-island-national-pasture-productivity / ) or inJames & Wells ref: 325511

[0127] our example accessing satellite data directly such from Sentinel 2 data on the Microsoft Planetary Computer (MCP).

[0128] In (https: / / lris.scinfo.org.nz / layer / 105112-north- island-national-pasture-productivity / ) the pasture production map is produced by averaging annual pasture biomass estimates over 4 years. However other forms of annual pasture production can be used such as in our case deriving a median NDVI product between two custom thresholds, 0 - 0.85 and using updated imagery between Jan 2022 and Jan 2025. This interval coincides with the new processing of sentinel tiles on the MCP. It is planned to update the layer on a sub year or yearly basis.

[0129] In our system we use our own mosaicking of GAOIs of interest or the GAOI of interest can be selected from the Landcare website and added into a GIS system such as QGIS (https: / / www.qgis.org / ) and then categorised mapped sections for pasture production across the GAOI can be created by applying a coloured symbology to each pasture production value (for example: 2-18 t / ha).

[0130] Pasture production shown here in Figure 2A is derived from Normalised Difference Vegetation Index (NDVI) cloud free satellite images regressed against mean annual pasture biomass production (tonnes / ha / year) from a limited number of calibration sites with accurate biomass measurements and geo-referencing. The specific methodology that landcare used and calibration site data can be obtained here (https: / / www.mdpi.com / 2072-4292 / 13 / 8 / 1481). However, in our system we have used only some calibration site data on public record (those deemed suitable due to land use change and timeframes) and added in our own calibration sites that are not part of the paper, these historical / new calibration sites express data in tDM / ha / yr over a 365 day period.

[0131] In our system we do not fit a linear regression (between NDVI and t / ha / yr) as the Landcare paper states as we have determined the NDVI curve is not a linear fit when we consider hill / high country pasture production.

[0132] In summary: our system developed a national map of pasture productivity, measured in dry matter yield per unit area and time, to evaluate pasture production and land-use suitability.

[0133] Using three years of Sentinel-2 satellite imagery and pasture yield measurements, the model employs the NDVI median filter. These estimate annual pasture productivityJames & Wells ref: 325511

[0134] across New Zealand's grasslands with a standard error of prediction of less than 2.2 t / ha / y.

[0135] The map reveals spatial variations in pasture yield and classifies grasslands into production levels on a national scale.

[0136] Specifically, for mapped section A this number is 13.8 tDM / ha and for mapped section B this number is 20 tDM / ha.

[0137] c) Assessing amount of pasture able to be eaten by adjusting pasture production

[0138] The pasture production data from step b) above is adjusted by taking into account stock utilisation of each mapped section (see Figure 2B) within the GAOL

[0139] For this example, pasture production for the mapped sections which are on terrain which is flat / rolling country pasture production was adjusted by selection of a single utilisation factor of 0.8 for typical dairy stock animals based on average pasture utilisation (see Table 1 below).

[0140] Table 1: Pasture Utilisation Factors

[0141] Exceptional utilisation:

[0142] Average Pasture Utilisation

[0143]

[0144] Poor utilisation would be

[0145] Pasture production can also be adjusted according to further information from the below PDF link:

[0146] https: / / www.dairynz.co.nz / media / m4knoxds / facts and figures chapter 4 updated dec ember 2021.pdf

[0147] as required for a given agronomic outcome.

[0148] Specifically, the assessment of the amount of pasture able to be eaten by a typical stock unit considering pasture type being grown is calculated as follows:

[0149] Annual pasture production (from step b) for an individual mapped section withing the GAOI x utilisation factor = feed available to be eaten

[0150] This calculation creates a pasture available to be eaten (tDM / ha) layer across the GAOI.James & Wells ref: 325511

[0151] For the GAOI in Figure 1 the calculation of the amount of pasture available for mapped sections A and B to be eaten is:

[0152] For mapped section A:

[0153] 13.8t DM / ha x 0.8 = 11 tDM / ha

[0154] For mapped section B:

[0155] 20 tDM / ha x 0.8 = 16 tDM / ha

[0156] The result of this calculation being applied over the mapped sections of the GAOI is shown in Figure 3.

[0157] d) Part 1 - Calculating the number of stock required

[0158] The number of stock (measured in stock units) supported by the GAOI is a critical piece of information required to calculate the maintenance nutrient requirements across the GAOI. Maintenance nutrient requirements are the replacement nutrient otherwise exported / removed / lost over 365 days from the GAOI. The number of stock in stock units (SU) supported by the GAOI is calculated by dividing the pasture available to be eaten across the GAOI by 550kgDM / yr. The 550kgDM / yr figure represents the typical amount of feed eaten by one stock unit across 365 days https: / / www.landcareresearch.co.nz / assets / Publications / Ecosystem-services-in-New- Zealand / 1 5 Morris.pdf. The calculation is shown below:

[0159] Pasture available to be eaten divided by typical SU intake (~550kgDM / yr) equals the number of stock supported by the GAOI.

[0160] For example, for mapped section A as shown in Figure 4, the number of stock supported would be: 11 tDM / ha x 1000 / 550kgDM / yr = 20 SU / ha.

[0161] For mapped section B as shown in Figure 4, the number of stock supported would be: 16 tDM / ha x 1000 / 550kgDM / yr = 29 SU / ha

[0162] Part 2 - Producing a nutrient / agrochemical application map

[0163] To produce a nutrient / agrochemical map that maintains nutrient levels over 365 days for the GAOI, the number of stock units supported by the GAOI over 365 days is multipliedJames & Wells ref: 325511

[0164] by known figures for maintenance nutrient requirements (kg / ha) in relation to stocking rate (as measured by stock units). Average dairy cow production from Ravensdown technical note: 17.4 can be found in Table 2.

[0165] Table 2: Stocking Rate Conversion

[0166] Tech note

[0167]

[0168] Approximate stock unit (suj equivalents” for dairy cows**

[0169] MS / ha

[0170] MS / ha MS / ha I MS / ha MS / ha MS / ha MS / ha

[0171] 1

[0172] 250 300 J 350 400 450 500 550 LWT(kg)

[0173] 350 6.4 6.9! 7.5 8.0

[0174] 400 6.7 7.3 7.8 8.4 8.9

[0175] 450 7.1 7.6 8.2 8.7 9.3 9.8

[0176] 500 7.5 8.0 8.5 9.1 9.4 10.2 10.7 550 7.8 8.4 8.9 9.5 10.0 10.5 11.1

[0177]

[0178] i LWT ’ Liveweight

[0179] 2 MS = milk solids

[0180] * Based on “down ths throat" requirements (and not allowing for wastage during grazing) assuming 1 kg DM pasture =

[0181]

[0182] 11 MJME •’ Adapted from Deuce! " Feed4Profit" Information

[0183] Stocking rate conversion table for dairy which was adapted from Dexcel “Feed4Profit” information (Table 2) is used to convert to stock units using the following conversion:

[0184] An average dairy cow produces 400 kg MS / ha and has a liveweight of 400 kg for a Jersey cow which is equal to 8.4 stock units.

[0185] The calculation shown below continues the example from above for a mapped section within the GAOI.

[0186] To produce a nutrient / agrochemical map that maintains nutrient levels over 365 days for the GAOI the number of stock units supported by the GAOI over 365 days is multiplied by known figures for maintenance nutrient requirements for livestock (kg / ha) in relation to stocking rate (as measured by stock units) from published literature https: / / www.fertiliser.org.nz / download / 166120 / Dairy%20Farms%20booklet24(Digital).pdfJames & Wells ref: 325511

[0187] As can be seen for mapped section B as indicated on Figure 4, 29 SU is approximately 29 SU / 8.4 = 3.5 cows / ha. Taking the midpoint for P (phosphorus) from Table 3 below, this is calculated as 41+59 / 2 = 50 kg P / ha is required to maintain the phosphorus levels in this area for 365 days.

[0188] Table 3: Maintenance Nutrient Requirements (kg / ha / yr) in relation to stocking rate in the range of 2-4 cows / ha

[0189]

[0190] A more refined maintenance calculation derived from known figures has been developed which for phosphorus is kg P / ha = 1.723 x SU + 0.4598

[0191] Which allows maintenance nutrient levels to be calculated across a continuous range of stocking rates.

[0192] The resulting maintenance P map in kg P / ha in Figure 5 shows the P required to maintain the soil nutrient P concentrations over 365 days at the current calculated stocking rate for the GAOI is shown.

[0193] Taking into account the earlier calculation of: 29 SU requiring 50 kg P / ha to maintain the phosphorus levels in this area for 365 days.

[0194] For dairy, the nutrients being returned within the effluent discharge GAOI (identified by a shapefile at step a) and shown in Figure 1B should be subtracted from the maintenance nutrient requirement for the effluent GAOI, the step shown in e) to account for nutrients being returned in the effluent to the effluent discharge GAOI over 365 days.

[0195] In current form, the nutrient returned as effluent over 365 days can be calculated without user input using a factor of 0.7 kg P / cow / year (this being the mass of P that a cow produces as effluent in the milking shed and yards which is returned to the pasture as effluent discharge).James & Wells ref: 325511

[0196] Example:

[0197] 100ha farm GAOI with a 15ha effluent discharge area as shown in Figure 1B.

[0198] Farm GAOI has on average 16.17tDM feed available to be eaten / ha = 1,617,000 kg DM (from d)

[0199] equals 1,617,000 / 550 = 2,940 SU / 8.4 = 350 cows on farm GAOI

[0200] 350 cows * 0.7kgP / cow / year = 245kgP; divided by size of effluent discharge GAOI (15ha) = 16.3 kg P / ha applied as effluent on the effluent discharge GAOI

[0201] Maintenance nutrient required for 365 days corrected for nutrients being returned as effluent over 365 days to the effluent discharge GAOI

[0202] = 50kg P / ha - 16.3 kg P / ha = 33.7 kg P / ha

[0203] e) Obtaining soil nutrient testing data inside and / or outside of GAOI, and / or remote sensed data for mapped sections forming GAOI.

[0204] In the examples further exemplified below physical paddock soil sampling is undertaken as depicted in Figures 6A - 6C is used to obtain: zonal, paddock and grid samples, but it should be appreciated that other soil sampling strategies (such as inter paddock sampling) or remote sampling or indirect strategies (such as optical inference including multispectral / hyperspectral surveys of the GAOI as a polygon vector file and / or machine learning) could also be used singly or together.

[0205] Hard to access terrains particularly sloped terrains may require sampling outside the GAOI to obtain an accurate soil nutrient measurement.

[0206] Remote sense data may be used in place of physical sampling if the accuracy of the remote sensed data is within acceptable levels of difference to physical sampling (e.g. within a 5 % - 10 % margin of error).

[0207] In a preferred embodiment machine learning (also including generative ai) may be employed to:

[0208] analyse environmental / physical data received;James & Wells ref: 325511

[0209] make predictions based on actual data received or artificially generated and compare to previous outcomes from implementation of the method of the present invention;

[0210] utilise new information on how to interpret environmental / physical data;

[0211] create feedback loops to assist with making predictions;

[0212] all to improve machine learning outcomes when coupled with the present invention.

[0213] f) Using soil nutrient testing data from step e) to create one or more nutrient surface maps for nutrients of interest taking into account the mapped sections within the GAOL

[0214] Nutrient / soil acidity surface layers which estimate the soil concentrations at a nominated depth (such as 7.5cm) over the GAOI such as soil Olsen P, QTK (potassium), or soil pH are derived using the data from step e) shown respectively in Figures 7 - 9.

[0215] Nutrient values contained within a field / paddock (i.e., mapped section) within the GAOI are extrapolated to cover the complete effective area of the field / paddock, allowing each field / paddock to be categorized by their respective nutrient level values.

[0216] g) Using data from d) and augmented with data from f) with knowledge of optimal levels of nutrient for crop / pasture growth, to create optimised GAOI application maps.

[0217] Here the nutrient / agrochemical application map obtained from step d) providing the maintenance nutrient map represented in Figure 5 is augmented with the soil nutrient data from step f) - represented in Figures 7 - 9 showing the nutrient / soil and acidity surface layers and estimated soil concentrations over the GAOI. With this knowledge of biological (soil test ranges which will produce at least 97% of relative pasture production) or economic optimal levels of nutrient for crop / pasture growth, optimised nutrient application maps that satisfy agronomic or economic criteria are produced over the mapped sections of the GAOI.

[0218] For example, a mapped section identified as a low pasture yielding area with a high soil nutrient status may have a reduced nutrient application recommendation compared to a mapped section having a potential high pasture yield and low soil nutrient status.James & Wells ref: 325511

[0219] As discussed earlier - mapped section B with 29 SU requires 50 kg P / ha to maintain nutrient status but currently has a soil Olsen P level of 65 so is above the biological optimum and therefore P nutrient is not required for the mapped section for the next 365 days.

[0220] Also as discussed earlier -mapped section A with 20 SU requires 35 kg P / ha to maintain and is currently at a soil Olsen P level of 21 so at lower end of biological optimum and thus requires 35 kg of P / ha nutrient for next 365 days to maintain its current soil fertility. Ideally, further P nutrient could be recommended to ensure nutrients levels move to upper end of biological optimum.

[0221] Example 2 - Livestock (sheep / beef / deer) - topography hill country terrain

[0222] The method steps a) to e) of the present invention substantially as described above in relation to a livestock farm example are outlined below:

[0223] a) Selecting a geographic area of interest (GAOI).

[0224] The farm shown in Figure 12 is obtained from a Polygon vector file (in this case Shapefile) which has been selected, from www.hawkeye.farm a publicly accessible website map database which has a large repository of farm boundary files. To further refine this map, or create a new map, a user can upload a file to the Hawkeye database or create one by manually digitising a boundary around each field in a GAOI on a farm found in the Hawkeye database. The map in Figure 12 was selected by requesting the farm in the database after it had been digitised or uploaded.

[0225] The shapefile currently uses a New Zealand Transverse Mercator (NZTM) projection which flattens the spherical shape into a flat map surface and allows metric units of measure for more information refer the following link:

[0226] New Zealand Transverse Mercator 2000 (NZTM2000) I Geodetic Guidance.

[0227] This process for selection and refinement of the GAOI is the start of an automated chain to produce a maintenance nutrient plan or optimised nutrient plan for the GAOI requiring no further user input.

[0228] b) Obtaining pasture productivity layersJames & Wells ref: 325511

[0229] In New Zealand this can be achieved by accessing the following Landcare website which has a repository of information for pasture productivity throughout New Zealand (https: / / lris.scinfo.org.nz / layer / 105112-north-island-national-pasture-productivity / ) or in our example accessing satellite data directly such from Sentinel 2 data on the Microsoft Planetary Computer (MCP).

[0230] In (https: / / lris.scinfo.org.nz / layer / 105112-north-island-national-pasture-productivity / ) the pasture production map is produced by averaging annual pasture biomass estimates over 4 years. However other forms of annual pasture production can be used such as in our case deriving a median NDVI product between two custom thresholds, 0 - 0.85 and using updated imagery between Jan 2022 and Jan 2025. This interval coincides with the new processing of sentinel tiles on the MCP. It is planned to update the layer on a sub year or yearly basis.

[0231] In our system we use our own mosaicking of GAOIs of interest or the GAOI of interest can be selected from the Landcare website and added into a GIS system such as QGIS (https: / / www.qgis.org / ) and then categorised mapped sections for pasture production across the GAOI can be created by applying a coloured symbology to each pasture production value (for example: 2-18 t / ha).

[0232] Pasture production shown here in Figure 2A is derived from Normalised Difference Vegetation Index (NDVI) cloud free satellite images regressed against mean annual pasture biomass production (tonnes / ha / year) from a limited number of calibration sites with accurate biomass measurements and geo-referencing. The specific methodology that landcare used and calibration site data can be obtained here (https: / / www.mdpi.com / 2072-4292 / 13 / 8 / 1481).

[0233] However, in our system we have used only some calibration site data on public record (those deemed suitable due to land use change and timeframes) and added in our own calibration sites that are not part of the paper.

[0234] In our system we have do not fit a linear regression as the Landcare paper states as we have determined the NDVI curve is not a linear fit when we consider high country pasture production.

[0235] In summary: our system developed a national map of pasture productivity, measured in dry matter yield per unit area and time, to evaluate land-use suitability. Using three years of Sentinel-2 satellite imagery and pasture yield measurements, the model employs theJames & Wells ref: 325511

[0236] filtered NDVI median from the time series and regressed with tDM / ha from the calibration sites.

[0237] This estimated mean annual pasture productivity across New Zealand's grasslands has a standard error of prediction of less than 2.2 t / ha / y.

[0238] The map reveals spatial variations in pasture yield and classifies grasslands into production levels on a national scale.

[0239] Specifically, for mapped section A this number is 13.8 tDM / ha and for mapped section B this number is 20 tDM / ha.

[0240] c) Assessing amount of pasture able to be eaten by adjusting pasture production

[0241] Pasture production data from step b) above is adjusted to take into account pasture utilisation (consumption) to show mapped sections of the GAOI categorized according to stock pasture utilisation to form a pasture utilisation map (Figure 15).

[0242] Similar to dairy GAOI, pasture production for a hill country GAOI is adjusted by a utilisation factor. Specifically, for a hill country GAOI, pasture production is adjusted by three slope categories (measured at a 15m-by-15m resolution as shown in Figure 14) as follows:

[0243] • 0-12 degrees slope, pasture production is assigned an 80% utilisation factor;

[0244] • 12-26 degrees slope, pasture production is assigned a 70% utilisation factor; and

[0245] • > 26 degrees of slope, pasture production is assigned a 60% utilisation factor.

[0246] Specifically, the assessment of the amount of pasture able to be eaten by a typical stock unit considering pasture type being grown is calculated as follows:

[0247] Annual pasture production (from step b) x utilisation factor = feed available to be eaten

[0248] This calculation creates a pasture available to be eaten (tDM / ha) layer across the GAOI.

[0249] The result of this calculation being applied over the mapped sections of the GAOI is shown in Figure 16.James & Wells ref: 325511

[0250] d) Part 1 - Calculating the number of stock required

[0251] The number of livestock (measured in stock units) supported by the GAOI is a critical piece of information required to calculate the maintenance nutrient requirements across the GAOI.

[0252] Maintenance nutrient requirements are the replacement nutrient otherwise exported / removed / lost / immobilised over 365 days from the GAOI.

[0253] The number of livestock in stock units (SU) supported by the GAOI is calculated by dividing the pasture available to be eaten across the GAOI by 550kgDM / yr.

[0254] The 550 kg DM / yr figure represents the typical amount of pasture eaten by one stock unit across 365 days https: / / www.landcareresearch.co.nz / assets / Publications / Ecosystem- services-in-New-Zealand / 1 5 Morris.pdf. The calculation is shown below:

[0255] Pasture available to be eaten divided by typical SU intake (~550kgDM / yr) equals the number of stock supported by the GAOI.

[0256] For example, for mapped section C as indicated on Figure 15, the number of stock unit supported would be 11 tDM / ha x 1000 / 550kgDM / yr = 20 SU / ha.

[0257] For example, for mapped section D as indicated on Figure 15, the number of stock unit supported would be 5.5 tDM / ha x 1000 / 550kgDM / yr = 10 SU / ha.

[0258] Part 2 - Producing a nutrient / agrochemical application map

[0259] To produce a nutrient / agrochemical map that maintains nutrient levels over 365 days for the GAOI the number of stock units supported by the GAOI over 365 days is multiplied by known figures for maintenance nutrient requirements for livestock (kg / ha) in relation to stocking rate (as measured by stock units) from published literature https: / / www.fertiliser.org.nz / download / 166965 / Sheep%20%20and%20%20Beef%20Boo klet_FINA24L.pdf

[0260] For an area which has 10 SU (i.e., mapped section D) then taking the midpoint for P (phosphorus) maintenance rates for livestock farms from Table 4 below, this is calculated as 10+22 / 2 = 16 kg P / ha is required to maintain the phosphorus levels in this area for 365 days.James & Wells ref: 325511

[0261] Table 4: Maintenance Nutrient Requirements (kg / ha / yr) in relation to stocking rate in the range of 7-22 stock units / ha

[0262] 7 6-18 0-21 6-19

[0263] 10-22 0-28 8-25

[0264] 13 15-23 0-35 10-29

[0265] 16 21-34 0-41 13-33

[0266] 19 28-41 0-48 15-37

[0267]

[0268] 22 34-44 0-54 17-41

[0269] A more refined maintenance calculation derived from known figures has been developed which for phosphorus is kg P / ha = 1.723 x SU + 0.4598

[0270] Which allows maintenance nutrient levels to be calculated across a continuous range of stocking rates.

[0271] The resulting maintenance P map in kg P / ha in Figure 18 shows the P required to maintain the soil nutrient P concentrations over 365 days at the current stocking rate for the GAOL

[0272] e) Obtaining soil nutrient testing data inside and / or outside of GAOI, and / or remote sensed data for mapped sections forming GAOI.

[0273] In the examples further exemplified below physical in situ soil sampling is undertaken as depicted in Figure 19 across a livestock hill country GAOI. Specifically, soil testing data is sampled in a straight line with the locations of the line recorded by GPS at the beginning and end of the sampling line (transect).

[0274] Soil tests are analysed for chemical and physical measurements include soil pH, Olsen P, calcium, magnesium, potassium and sodium, organic sulfur, sulfate and bulk density.

[0275] In addition to obtaining soil nutrient testing data inside and / or outside the GAOI, further data - in preferred embodiments - may also be obtained at this step including:

[0276] remotely sensed imagery such as optical inference including multispectral / hyperspectral surveys of the GAOI as a polygon vector file and / or machine learning, as well asJames & Wells ref: 325511

[0277] - spatial layers such as rainfall, slope, soils, pasture production, P retention, elevation, solar radiation which are freely available and derived and interpolated from measurements which may or not cover the GAOL

[0278] In preferred embodiments data may be obtained from spatial layers such as rainfall, slope, soils, pasture production, P retention, elevation, solar radiation which are freely available and derived and interpolated from measurements which may or not cover the GAOL

[0279] In a preferred embodiment machine learning (also including generative ai) may be employed to:

[0280] analyse environmental / physical data received;

[0281] make predictions based on actual data received or artificially generated and compare to previous outcomes from implementation of the method of the present invention;

[0282] utilise new information on how to interpret environmental / physical data; create feedback loops to assist with making predictions;

[0283] all to improve machine learning outcomes when coupled with the present invention.

[0284] f) Using the data from step e) to create one or more nutrient surface maps for nutrients of interest taking into account the mapped sections within the GAOL

[0285] Nutrient / soil acidity surface layers which estimate the soil concentrations at a nominated depth (such as 7.5cm) over the GAOI such as soil Olsen P, or soil pH are derived using the data from step e) shown respectively in Figures 20 and 21.

[0286] Nutrient values contained within a field / paddock (i.e., mapped section) within the GAOI are extrapolated to cover the complete effective area of the field / paddock, allowing each field / paddock to be categorized by their respective nutrient level values.

[0287] g) Using data from d) and augmented with data from f) with knowledge of optimal levels of nutrient for crop / pasture growth, to create optimised GAOI application maps.James & Wells ref: 325511

[0288] Here the data obtained from step d) providing the maintenance nutrient map represented in Figure 18 is augmented with the soil nutrient data from step f) -represented in Figures 20 and 21 showing the nutrient / soil and acidity surface layers and estimated soil concentrations over the GAOL

[0289] With this knowledge of biological (soil test ranges which will produce at least 97% of relative pasture production) or economic optimal levels of nutrient for crop / pasture growth, optimised nutrient application maps that satisfy agronomic or economic criteria are produced over the mapped sections of the GAOL

[0290] With this knowledge of biological or economic optimal levels of nutrient for crop / pasture growth, optimised nutrient application maps that satisfy agronomic or economic criteria are produced over the GAOI as shown in Figure 22.

[0291] For example, a mapped section within a GAOI identified as a low yielding area with high nutrient status should have a reduced application compared to a mapped section within a GAIO with a potential high pasture yield and current low nutrient status.

[0292] The nutrient application map as shown in Figure 22 is then converted into a product application map as shown in Figure 23.

[0293] Specifically, nutrients required are converted to products required by taking in to account the nutrient content of the product(s) and by removing environmentally sensitive areas at an appropriate resolution for application as shown in the following examples in Table 5 below.

[0294] Table 5: Nutrients Required v. Products Required Conversion

[0295] Superphosphate Tons of

[0296] area

[0297] rate (kg / ha) at superphosphate

[0298] (ha)

[0299] 9% P content

[0300] 10

[0301] 133 75.33

[0302] 100.24

[0303] 167 613.14

[0304] 240

[0305] 222 1082.13

[0306] 89.5

[0307] 278 322.06

[0308]

[0309] James & Wells ref: 325511

[0310] 27.6

[0311] 333 82.93

[0312] 4.1

[0313] 389 10.56

[0314] 474

[0315] totals 2186.2

[0316]

[0317] h) Applying nutrients in amounts determined from previous step g to the mapped sections within the GAOL

[0318] The variable rate maintenance application map shown in figure 24 matches the ability of aircraft to apply fertiliser at the correct rate and placement.

[0319] Small peninsulas and protrusions are removed, and minimum area of polygons is accounted for. Parallel flight lines are recorded as the aircraft makes each flight path (figure 25).

[0320] The on-board field computer receives the rate from the GNSS location and variable rate application map and provides this to an ECU that controls the opening of a gate.

[0321] The gate is opened / closed ahead of time to deliver the flow rate at the chosen swath width and at the aircraft’s forward velocity.

[0322] As the aircraft flies along the flight line, the on-board computer records the GNSS position, gate opening, swath width and application rate (among other things).

[0323] This information can be used to generate the proof of release maps by buffering the flight lines by the swath width and filling the mapped sections of the GAOI with the application rate attribute (figure 26).

[0324] The proof of release map can be colored the same as the Variable Rate Application map to determine how well the aircraft delivered to the prescribed rates. Figure 27 shows the coverage of a ground application for a dairy farm.

[0325] Ground application can follow parallel paths as in the aerial application, but ground application can also apply a continuous application by turning at the end of the paddocks maintaining application and filling in the paddock coverage quicker than if it simply covers the paddock in an up and downward pattern.James & Wells ref: 325511

[0326] An aerial application is not encumbered by paddock boundaries and fences whereas a ground application is constrained by the shape of the paddock.

[0327] The invention detailed herein may provide one or more advantages over the existing agricultural land use, or at least offer the public a useful choice.

[0328] The invention as detailed above may also be said broadly to consist in the parts, elements and features referred to or indicated in the specification of the application, individually or collectively, in any or all combinations of two or more of said parts, elements or features.

[0329] Aspects of the present invention have been described by way of example only and it should be appreciated that modifications and additions may be made thereto without departing from the scope thereof as defined in the appended claims.James & Wells ref: 325511

[0330] REFERENCES

[0331] Amies, A. C., Dymond, J. R., Shepherd, J. D., Pairman, D., Hoogendoorn, C., Sabetizade, M., & Belliss, S. E. (2021). National Mapping of New Zealand Pasture Productivity Using Temporal Sentinel-2 Data. Remote Sensing, 13(8), 1481. https: / / doi.org / 10.3390 / rs13081481.

[0332] Morris S. T. (2013). Sheep and beef cattle production systems. In Dymond JR ed. Ecosystem services in New Zealand - conditions and trends. Manaaki Whenua Press, Lincoln, New Zealand. doi:10.7931 / DL1MS3.

Claims

James & Wells ref: 325511WHAT WE CLAIM IS:

1. A method of providing precise nutrient management over a set 12 month period on a non-rotational basis in terms of what agricultural interventions are, or are not, required for obtaining pasture / crops for feeding grazing animals for a future 12 month period following said agricultural intervention being deployed, the method comprising the following steps:a) Selecting a geographic area of interest (GAOI);b) Obtaining pasture production map with categorized or graduated mapped sections for pasture production across the GAOI;c) Assessing amount of pasture able to be eaten by a typical stock unit over said set 12-month period by adjusting pasture production map from step b) to take into account stock utilization for each mapped section within the GAOI; d) Calculating - from said information from step c) - number of stock required to consume pasture, taking into account stock utilisation to produce a nutrient / agrochemical application map that replaces nutrient otherwise exported / removed / lost from the GAOI, to maintain sufficient pasture for the stock numbers over said set 12 month period on a non-rotational basis;e) Obtaining soil nutrient testing data inside and / or outside of GAOI, and / or remote sensed data related to soil for mapped sections forming GAOI;f) Using the data from step e) to create one or more nutrient surface maps for nutrients of interest taking into account the mapped sections within the GAOI;g) Using the soil nutrient testing data and / or the remote sensed data related to soil data from e) and augmented with data from f) with knowledge of optimal levels of nutrient for crop / pasture growth, to create optimised GAOI nutrient application maps; wherein said stock units are maintained on the GAOI over said set 12- month period without the need to rotate the stock unit; andh) Extracting from said nutrient maps what nutrients and the amount of said nutrients that need to be applied to said GAOI or mapped sections therein.

2. A method as claimed in claim 1 wherein the method includes the further step of:James & Wells ref: 325511i) Applying nutrients in amounts determined from previous step g) to the mapped sections within the GAOL3. A variable rate maintenance application map based on optimised GAOI nutrient map which is based on a maintenance application map which details levels of nutrient from crop / pasture growth for mapped sections within the GAOI wherein the map obtains details of nutrients required that are assessed from categorized pasture production maps, assessing amount of pasture that can be eaten by a typical stock unit, assessing stock utilization and taking into account soil testing data.

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