Outils d’aide à la décision pour la gestion des fourrages herbagers dans les exploitations laitières wallonnes: adoption et perspectives

peer reviewedDescription of the subject. Many decision support tools (DSTs) have been developed to help dairy farmers optimally manage the high variability in the quality and availability of grass-based fodder, but their adoption rate remains low. Objectives. The objective was to characterize and understand the adoption rate of DSTs related to using grass-based fodder. Methodology. A sample of 61 Walloon (Belgium) dairy farmers responded to an online survey concerning their current use of 23 DSTs related to using grass-based fodder either directly (pasture or grassland) or indirectly (feeding or techno-economic), as well as barriers to and incentives for adopting them, their current interest in DSTs, and satisfaction with the guidance on using these DSTs. Results. Pasture management DSTs were used the least, even though farmers were the most interested in them. Farmers used simple indicators rather than software or automated tools. Farmers indicated that DSTs were too expensive and time consuming, even if they could ultimately save them time and money. Continuing education is lacking. Four types of users were identified who influence the use of DSTs: high user no grazing (H-NG), high user traditional or technical grazing (H-T/TG), low user traditional grazing (L-TG), and moderate user organic (M-ORG). Conclusions. Communicating with end-users during each step of DST development would help (1) identify the specific needs of a diverse set of dairy farmers and (2) develop DSTs that better correspond to their practices. More long-term guidance is required to inform farmers about existing DSTs and to transfer the knowledge required to use them.EFFOR

Contribution of milk mid-infrared spectrum to improve the accuracy of test-day body weight predicted from stage, lactation number, month of test and milk yield

A regular and repeated recording of body weight (BW) is useful information for herd management. BW can bepredicted regularly from animal characteristics such as age, lactation number, or lactation stage. Those traits areunfortunately animal unspecific. Adding animal specific information, which can be easily obtained on a largescale, to the BW prediction would be of utmost importance. There are good scientific reasons to suspect linksbetween BW and animal specific characteristics, available in a repeated fashion, such as milk yield and milkcomposition. This study aimed to demonstrate the feasibility of predicting test-day BW from stage, lactationnumber, month of test, milk yield and mid-infrared spectra, representing milk composition. Five models weretested initially from 721 BW records collected in 6 herds: day in milk + number of lactation (equation 1a);equation1a + milk yield (equation 1b); only spectral data (equation 1c); equation 1c + equation 1a (equation2); equation 2 + milk yield (equation 3). Then 3 other equations included the same explicative variables, exceptthat the spectral data were regressed using second order Legendre Polynomials (PL) to take into account changesof spectral data within lactation. Equation 1a and 1b were built using linear regressions and equation 1c until 3were built using partial least square regressions. These 3 last equations had a higher number of factors. Adding ofMIR data in the equation increased of 7% the values of cross-validation R² (R²cv). Potential BW outliers werediscarded using a residual analysis based on equation 3. From 662 records, the following statistical parameterswere obtained: the calibration coefficient of determination (R²c) = 0.65, R²cv = 0.61, calibration root meansquared error of prediction (RMSEP)=38 kg, and RMSEPcv=40 kg. Low variation of R²c and RMSEPc valuesobtained from the herd validation confirmed the herd independence of predictions. However, large variabilitywas observed for RMSEPv (37 to 64 kg) suggesting the need to increase the dataset in order to improve therobustness of the equation. By applying the equations on a large spectral database, it was confirmed that theaddition of MIR data allows to better model the BW evolution within lactation. Based on these preliminaryresults, and if a larger validation confirms thesefindings, this approach could be used to develop equations thatare better able to assess BW throughout lactation(s), BW being an important element for management andselection tools