Tomas Persson
Forsker
(+47) 466 30 485
tomas.persson@nibio.no
Sted
Særheim
Besøksadresse
Postvegen 213, NO-4353 Klepp stasjon
Sammendrag
Ensiling is a common mode of preservation of animal feed. In this process, the feed undergoes lactic acid fermentation in an anaerobic environment, which decreases pH and inhibits degradation of the feed and its nutritive value. Common silos include top loaded tower silos, side loaded bunker silos (also called horizontal silos), underground pit and trench silos, and bales and tubes wrapped in plastic film. Previous studies have revealed that the type of silo often have an impact on silage properties and feed value, but these effects can vary between silage materials. Silage density is another key factor for silage nutritive value and losses. Generally, high density results in smaller losses than low density, both in bunker silos and bales, but the density effect can also be influenced by properties of the ensiled material. The objectives of this literature review were to identify factors and conditions that can modify the effect of i) silage density, and ii) silo type on dry matter losses, leaching of nutrients, fermentation characteristics, silage feed value and mycotoxins contamination. A systematic literature search was carried out in in the Web of Science core collection platform of databases. Most studies showed positive correlations between silage density, and fermentation and feed value, and negative correlations with DM losses. The majority of these studies were conducted at laboratory scale and there was also a great variation in the magnitude of these effects. Further investigations at farm scale may provide more information about the consistency of these effects across experimental scales. The silo type comparisons indicate that silage bales, bags and tubes can be favourable for silage quality and dry matter preservation compared to bunker silos, but information on silo type effects on important crops such as maize is missing.
Sammendrag
Ensiling of whole-crop biomass of barley before full maturity is common practice in regions with a short growing season. The developmental stage of barley at harvest can have a large impact on yield and nutritive composition. The relationships between crop growth, environmental conditions and crop management can be described in process-based simulation models. Some models, including the Basic Grassland (BASGRA) model, have been developed to simulate the yield and nutritive value of forage grasses, and usually evaluated against metrics of relevance for whole-crop silage. The objectives of this study were to: i) modify the BASGRA model to simulate whole-crop spring barley; ii) evaluate the performance of this model against empirical data on dry matter (DM) yield and nutritive value attributes from field experiments, divided into geographical regions; and iii) evaluate DM yield, nutritive value and cutting date under current and future climate conditions for three locations in Sweden and four cutting regimes. Main model modifications included addition of a spike pool, equations for carbon (C) and nitrogen (N) allocation to the spike pool and equations for C and N translocation from vegetative plant parts to spikes. Model calibration and validation against field trial data from Sweden, including samples harvested from late anthesis stage to hard dough stage that were either pooled or divided into regions, showed better prediction accuracy, evaluated as normalised root mean squared error (RMSE), of neutral detergent fibre (NDF) (7.58–18.4%) than of DM yield (16.8–27.8%), crude protein (15.5–23.2%) or digestible organic matter in the DM (DOMD) (12.0–22.2%). Model prediction using weather data representing 1990–2020 and 2021–2040 climate conditions for three locations in Sweden (Skara, Umeå, Uppsala) showed lower DM yield, earlier harvest and slightly higher NDF concentration on average (across locations and developmental stage at cutting) when using near-future climate data rather than historical data. The model can be used to evaluate whole-crop barley performance under production conditions in Sweden or in other countries with similar climate, soils and crop management regimes.
Sammendrag
Context In high-latitude regions, variable weather conditions during the growing season and in winter cause considerable variation in forage grass productivity. Tools for predicting grassland status and yield, such as field measurements, satellite image analysis and process-based simulation models, can be combined in decision support for grassland management. Here, we calibrated and validated the BASic GRAssland (BASGRA) model against dry matter and Leaf area index data from temporary grasslands in northern Norway. Objective The objective of this study was to compare the performance of model versions calibrated against i) only region-specific ground data, ii) both region-specific ground and Sentinel-2 satellite data and, iii) field trial data from other regions. Methods Ground and satellite sensed data including biomass dry matter, leaf area index, and autumn and spring ground cover from 2020 to 2022 were acquired from 13 non-permanent grassland fields at four locations. These data were input to BASGRA calibrations together with soil and daily weather data, and information about cutting and nitrogen fertilizer application regimes. The effect of the winter season was taken into account in simulations by initiating the simulations either in autumn or in early spring. Results Within datasets, initiating the model in spring resulted in higher dry matter prediction accuracy (normalised RMSE 22.3–54.0 %) than initiating the model in autumn (normalised RMSE 41.1–93.4 %). Regional specific calibrations resulted in more accurate biomass predictions than calibrations from other regions while using satellite sensing data in addition to ground data resulted in only minor changes in biomass prediction accuracy. Conclusion All regional calibrations against data from northern Norway changed model parameter values and improved dry matter prediction accuracy compared with the reference calibration parameter values. Including satellite-sensed data in addition to ground data in calibrations did not further increase prediction accuracy compared with using only ground data. Implications Our findings show that regional data from farmers’ fields can substantially improve the performance of the BASGRA model compared to using controlled field trial data from other regions. This emphasises the need to account for regional diversity in non-permanent grassland when estimating grassland production potential and stress impact across geographic regions. Further use of satellite data in grassland model calibrations would probably benefit from more detailed assessments of the effect of grass growth characteristics and light and cloud conditions on estimates of grassland leaf area index and biomass from remote sensing.
