Yield map­ping challenges 

The acreage per­for­mance of mod­ern har­vesters has improved con­tin­u­ous­ly over the past few years. This is one rea­son why com­bine and for­age har­vesters are often used to har­vest mul­ti­ple farms – machin­ery syn­di­cates and agri­cul­tur­al con­trac­tors have spe­cialised in pro­vid­ing these ser­vices. The data gen­er­at­ed dur­ing the har­vest­ing is extreme­ly impor­tant when plan­ning crop cul­ti­va­tion mea­sures. So that farm man­agers can use the data, yield map­ping pos­si­bil­i­ties have also evolved.

The har­vest yields from agri­cul­tur­al land gen­er­al­ly exhib­it some localised dif­fer­ences, the caus­es for which can be traced back to the soil char­ac­ter­is­tics. Oth­er fac­tors relate to the cul­ti­va­tion mea­sures applied, such as the nutri­ents pro­vid­ed and dif­fer­ences in sow­ing meth­ods. Yield data and oth­er types of data can be used to cap­ture these site-spe­cif­ic vari­a­tions. As a result, yield map­ping offers a data-dri­ven approach to deci­sion mak­ing that enables crops to be cul­ti­vat­ed site-specif­i­cal­ly. It is impor­tant to note, how­ev­er, that yield map­ping alone does not pro­vide defin­i­tive infor­ma­tion about the caus­es of yield vari­a­tions. Com­par­ing data from var­i­ous sources in order to draw valid con­clu­sions about the poten­tial of indi­vid­ual field sec­tions is a much more con­struc­tive approach. Solu­tions that detect growth dif­fer­ences in crops, for exam­ple, can be applied here. The Crop View com­po­nent offers one such solu­tion. It uses mul­ti-year satel­lite data to cal­cu­late the soil poten­tial and cre­ate appli­ca­tion maps. It also incor­po­rates soil sam­ple data, which is used to cre­ate appli­ca­tion maps too.

Mea­sure­ment sys­tems for deter­min­ing crop yields 

Data from mul­ti­ple crop years is extreme­ly rel­e­vant when it comes to cre­at­ing mean­ing­ful yield maps. This data pool is used to deter­mine zones that dif­fer with regards to the yield poten­tial. Before farm­ers can even start to cap­ture this data, they need to have the right mea­sure­ment tech­nol­o­gy in the form of sen­sors and weigh­ing sys­tems in place. Farm­ers have been record­ing yield quan­ti­ties and doc­u­ment­ing yield data for some time now. The prac­tice has been devel­op­ing in par­al­lel with tech­ni­cal advance­ments in sen­sor technology.

Anoth­er impor­tant aspect of yield map­ping is the pre­cise posi­tion­ing of mea­sure­ment points in the coor­di­nates sys­tem for each field. Tech­nolo­gies such as real-time kine­mat­ic (RTK) and dif­fer­en­tial glob­al posi­tion­ing sys­tems (DGPS) are used for this. They can define the posi­tion of a machine to the exact cen­time­tre. Improve­ments have also been made in this sec­tor over the years, enabling posi­tion data to be cap­tured with ever greater pre­ci­sion. DGPS works using sta­t­ic ref­er­ence sta­tions with pre­cise­ly defined posi­tions. Mea­sure­ment sta­tions are used to cor­rect and opti­mise GPS satel­lite posi­tion data. They are absolute­ly crit­i­cal in yield map­ping in order to eval­u­ate data. Var­i­ous sys­tems are avail­able for mea­sur­ing yield quantities.

Yield map­ping is most com­mon­ly used in arable farm­ing. High-tech com­bine har­vesters are equipped with a mul­ti­tude of crop analy­sis tools. Crop data is con­tin­u­ous­ly record­ed via light sen­sors and power/impulse mea­sure­ments in the machine’s grain ele­va­tor. They pro­vide infor­ma­tion about the grain vol­ume and mass. Oth­er sen­sors gen­er­ate infor­ma­tion about the crop’s mois­ture con­tent. The mea­sure­ments can be used to cor­rect yield data so that the over­all val­ues can be more accu­rate­ly illus­trat­ed. Data on routes, cut­ting widths, ground speed and mass flow allow con­clu­sions to be drawn about the acreage performance.

Appro­pri­ate tech­nolo­gies such as mobile and sta­tion­ary weigh­ing sys­tems are also avail­able for grass­land and maize har­vest­ing. Mobile sys­tems deter­mine the weight of the crop via sen­sors installed in the axles of trans­port trail­ers and grain wag­ons. Sta­tion­ary sys­tems can be used near the field or in the area around silos. How­ev­er, nei­ther of these sys­tems enable site-spe­cif­ic cul­ti­va­tion because the crop can­not be attrib­uted to an exact posi­tion on the field. To solve this prob­lem, for­age har­vesters record the vol­ume flow of the crop. The flow is deter­mined based on roller posi­tion data and the crop intake speed. Cal­i­bra­tion is required in order to accu­rate­ly deter­mine the crop yield. To do this, the actu­al har­vest­ed quan­ti­ty is weighed on the trans­port wag­on and then entered into the sys­tem via the for­age har­vester. The on-board com­put­er then uses these val­ues to deter­mine the yield data. For ultra-pre­cise results, the cal­i­bra­tion should be repeat­ed every time the crop vari­ety or field changes. The val­ues gen­er­at­ed for yield map­ping as part of this process can also be used for pre­ci­sion farm­ing measures.

With the help of DGPS, the con­tin­u­ous­ly record­ed data from sen­sors and mea­sur­ing instru­ments is assigned to a spe­cif­ic point on the field so that it can then be used for site-spe­cif­ic cul­ti­va­tion. As this process uses so-called ‘point data’, the val­ues used for yield map­ping must be trans­formed into field data. We’ll dis­cuss the chal­lenges aris­ing from the inter­po­la­tion process in the fol­low­ing chapter.

The chal­lenges of inter­po­lat­ing point data 

In order to define the per­for­mance of field sec­tions based on yield points, the poten­tial sources of error need to be incor­po­rat­ed into the cal­cu­la­tion. For exam­ple, mea­sure­ment errors can come from the data on the har­vester’s posi­tion, ground speed, cut­ting width and through­put mea­sure­ments, all of which are used for map­ping. Dif­fer­ences emerge in rela­tion to the cut­ting width because for var­i­ous rea­sons the effec­tive work­ing width is usu­al­ly low­er than the actu­al val­ue. When it comes to remain­ing areas in par­tic­u­lar, the actu­al val­ues fluc­tu­ate sig­nif­i­cant­ly below the pro­vid­ed data. Anoth­er source of error is the fact that the crop needs a cer­tain amount of time to reach the sen­sors. The amount of time required is incon­sis­tent and depends on the harvester’s design and the through­put of the crop. This, cou­pled with chang­ing ground speeds, results in the mea­sured val­ues being assigned to inac­cu­rate posi­tions. These devi­a­tions are dif­fi­cult to cal­cu­late and cor­rect. The ground speed of the har­vester also caus­es inac­cu­ra­cies due to the dif­fer­ent mea­sure­ment process­es in the wheel, radar and GPS sensors.

Nev­er­the­less, posi­tion data is now sig­nif­i­cant­ly less error prone thanks to the opti­mised mea­sure­ment process­es. Auto­mat­ic steer­ing sys­tems and pre­cise field plan­ning solu­tions also help to min­imise poten­tial dri­ving errors. The bot­tom line is that all this results in improved yield map­ping meth­ods. The col­lect­ed yield data also pro­vides a reli­able source of infor­ma­tion that enables site-spe­cif­ic cul­ti­va­tion in farming.

Yas­min Moehring

Tel. +49 30 25 93 29–901
Mobil +49 151 17 28 18 69
moehring@365farmnet.com