NutriVet (Czechia)

Estimating the optimum harvest time and yield embodies an essential food security factor. Vegetation indices have proven to be an effective tool for widescale in-field plant health mapping. A drone-based multispectral camera then conveniently allows acquiring data on the condition of the plant. This article examines and discusses the relationships between vegetation indices and nutritiolnal values that have been determined via chemical analysis of plant samples collected in the field. In this context, emphasis is placed on the normalized difference red edge index (NDRE), normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and nutritional values, such as those of dry matter. The relationships between the variables were correlated and described by means of regression models. This produced equations that are applicable for estimating the quantity of dry matter and thus determining the optimum corn harvest time. The obtained equations were validated on five different types of corn hybrids in fields within the South Moravian Region, Moravia, the Czech Republic.

The aim of this study was to validate selected precision livestock farming (PLF) methods of nutrition and feeding management of high-yielding Holstein dairy cows. In a feeding trial with 36 dairy cows, the effect of replacing 0.1 kg of sodium bicarbonate in the control total mixed ration (TMR-C) with 1 kg of wheat straw in the experimental total mixed ration (TMR-S) on the physiological status of cows and the amount of milk produced (milk yield, MY) was investigated. Feed intake time (FT), as measured using tensometric feed troughs (TFT), was significantly longer with TMR-S (188 min) than with TMR-C (157 min). Differences between TMR-C and TMR-S were not significant for FT or rumination time (RT), as measured by a sensor in the collar (VSC). There was only a weak correlation between the two technologies (TFT vs. VSC) for FT (r = 0.27). Differences between TMR-C and TMR-S were not significant for values measured in rumen fluid (pH, acid and ammonia levels) nor for values measured by sensors in the milking parlour (MY, fat and protein percentage of milk). Milk analysis in the laboratory showed that the cows fed TMR-C had higher urea (26.6 vs. 22.7 mg/100 ml) and free fatty acid (0.87 vs. 0.33 mmol/100 g) levels in milk. Moderate correlations were between TMR intake and MY (r = 0.55); between MY and milk fat (r = –0.46); between milk fat and milk protein (r = 0.63); and between milk fat and milk protein measured by sensors and in the laboratory (r = 0.47 and r = 0.42, respectively). In view of the above results, further research and data validation for each technology are needed.

The aim of this study is to compare the differences between four maize hybrids in terms of nutrient determination by portable near-infrared reflectance spectrometer (pNIRS) and chemical analysis; each of the hybrids was grown in the same locality from 2018 to 2020. The topic relates to the variability of the feed value of maize being an important feedstuff in livestock nutrition. The nutritional values determined by pNIRS in comparison with the chemical analysis were higher (P < 0.001) in starch and ash content but lower in dry matter, neutral detergent fibre and crude protein (CP) content. The digestibility levels of neutral detergent fibre and the net energy of lactation as well as the potential milk production per hectare in relation to each tonne of dry matter were also lower. According to this result, it would be necessary to calibrate all tested indicators for a given spectrometer. However, the pNIRS results are useful for evaluating nutrient variability; the standard deviation of the values found in pNIRS was mostly lower than that determined chemically. The pNIRS results are also useful for making practical adjustments to the total mixed rations when calculated from actual chemical analysis if the correlation between the two methods is used; the correlation between the pNIRS and chemical results was found to be significant (P < 0.05) in terms of all the indicators.