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Abstract Both in land evaluation and in water management quantitative methods, GIS and simulation modelling are well-known techniques for quantifying the effects of changes, such as land use or climate change. For hydrological management decisions information is often required on the effect of those decisions on agricultural production. To serve the needs of different types of users, like water authorities, provinces, drinking water companies and the National Department of Infrastructure and Water Management we developed a toolbox named WaterVision Agriculture as an instrument that can determine effects on crop yield and the farm economy as a result of drought, too wet or too saline conditions for both current and future climatic conditions. WaterVision Agriculture is based on the hydrological simulation model SWAP, the crop growth model WOFOST and farm management and economic assessments such as DairyWise for dairy farming. The WaterVision Agriculture (WVA) project resulted in two products, namely i) an easily applicable tool (also called the WVA-table) and ii) the operational models for hydrology and crop growth SWAP and WOFOST for calculating effects on field scale combined with calculating farm economic results and indirect effects. SWAP simulates water transport in the unsaturated zone using meteorological data, boundary conditions (like groundwater level or drainage) and soil parameters. WOFOST simulates crop growth as a function of meteorological conditions and crop parameters. Using the combination of these process-based models and methods for describing crop management and economic value we derived a meta-model, i.e. a set of easily applicable simplified relations for assessing crop growth as a function of soil type and groundwater level. These relations are based on multiple model runs for at least 72 soil units and the possible groundwater regimes in the Netherlands. The easily applicable tool (WVA-table) uses this meta-model. Applying the meta-model of WaterVision Agriculture should allow for better decisions on land use or soil and water management because the instrument can help to quantify the effects of changes in climate, land use, hydrological conditions or combinations of these effects on agricultural production. Highlights • Land evaluation methods are successfully used in water management. • WaterVision Agriculture (WVA) uses simulation models on hydrology and crop growth • WVA integrates modelling with farm management and economy • WVA allows for better decisions on climate robust soil and water management • For decisions on land use decision makers should use both WaterVision Agriculture and Nature. [ABSTRACT FROM AUTHOR]

Policy measures have been taken to improve water quality in the Netherlands. These measures include the abolishment of derogation, which allowed dairy farmers to go beyond the maximum application of 170 kg nitrogen (N) from organic fertiliser per hectare, and additional measures of the 7th Nitrates Action program. Grass-clover swards, known for their symbiotic N fixation, could be a strategy to deal with stricter N policies and can potentially improve the environmental sustainability and economic viability of Dutch dairy farms. The aim was to assess the effects of stricter N policies on the farm structure, farm income and environmental performance of a representative Dutch dairy farm on a sandy soil, and to assess the effect of incorporating perennial ryegrass-red white clover (GC rw) swards and perennial ryegrass-white clover (GC w) swards into the grassland management of this farm using a model. A whole-dairy farm linear programming model was used with the objective function to maximize farm income. The model was combined with a farm nutrient balance and life-cycle assessment to determine the impact on nutrient surpluses and greenhouse gas emissions. We modelled a representative Dutch dairy farm with perennial ryegrass (PRG) before and after implementing stricter N policies. Thereafter, the implications of implementing GC rw and GC w swards was assessed. Including the policy measures increased the share of maize land (+12%), decreased the number of dairy cows (-9 cows), reduced farm income (€-18,858 yr-1), led to similar greenhouse gas emissions (∼800 kg carbon dioxide equivalents (CO 2 -eq) per tonne (t) of fat and protein corrected milk (FPCM)), and resulted in a lower N surplus (-65 kg ha-1 yr-1) and phosphate surplus (-4.4 kg ha-1 yr-1) for a scenario with only PRG. The use of GC rw and GC w swards could partly compensate for the reduction in farm income (€+9,255 up to +14,706 yr-1). A combination of PRG, GC rw and GC w resulted in the highest farm income. The use of grass-clover swards only had the most positive effect on greenhouse gas emissions (767 kg CO 2 -eq t-1 FPCM) and N surplus (113 kg ha-1 yr-1). Sensitivity analysis showed the importance of yields and feed characteristics on the obtained farm income. Grass-clover swards might partly compensate for the negative economic consequences of stricter N policies for Dutch dairy farms. Furthermore, implementing grass-clover swards reduced the GHG emission intensity of milk, but not always nutrient surpluses per hectare of farm land. [Display omitted] • Grass-clover swards might be a strategy for dairy farmers to deal with stricter nitrogen policies in the Netherlands. • Economic and environmental effects of stricter nitrogen policies were quantified with a whole farm model. • Number of cows, labour income and nutrient surpluses decreased under stricter policies. • Grass-clover swards improved labour income and carbon footprint but not always nutrient surpluses. [ABSTRACT FROM AUTHOR]

• Yields in variety trials showed significant genetic gains in forage maize and perennial ryegrass. • On-farm yields showed significant positive trends for forage maize, and no trend for grass. • Forage maize yields in farming practice are lower than in variety trials, and the gap is widening. Grass and forage maize are important forage crops in ruminant production systems in the temperate regions in northwest Europe. High yields of these crops contribute to farm profitability and local provision of feed, and hence local circularity of biomass and nutrients. Variety choice is an important option to raise potential and actual yields. We analysed 40 years of perennial ryegrass and 25 years of forage maize yield data from Value of Culture and Use (VCU) experiments to determine genetic and non-genetic trends of yields in time. For maize, we calculated an annual genetic trend of +173 kg DM ha−1 and a non-genetic trend of +65 kg DM ha−1. Further analysis of the non-genetic trend showed that maize yields increased with increasing temperature sum during the growing season, and with earlier sowing. The feeding value of forage maize showed a genetic trend of +1.7 feed unit milk (VEM) kg DM−1 year−1. The annual genetic gain of perennial ryegrass was +44 kg DM ha−1. In the grass trials we found opposing non-genetic trends for cutting and grazing. Further analysis of the non-genetic trend showed that drought and the number of days with ground frost during the growing season had a negative effect on yield. We compared the average yields and trends in VCU trials with those of on-farm yields. The on-farm maize yields showed an annual trend of +195 kg DM ha−1. We estimated an average realisation of the genetic gains of 75 % in farming practice, implying a widening gap between genetic potential and on-farm yields. Averaged over the entire period, on-farm maize yields were 4.6 t DM ha−1 (24%) lower than the yields of the VCU trials. The average annual on-farm grass yields did not show any trend, and were 1.6 t DM ha−1 (13%) lower than the yields of the VCU trials. In conclusion, our study revealed significant positive genetic and varying non-genetic trends in DM yields of forage maize and perennial ryegrass, the two dominant forage crops in the Netherlands. On-farm yields showed significant positive trends for forage maize, but no trend for grassland. [ABSTRACT FROM AUTHOR]

Dairy farms with intensive grazing systems combine grazing with supplemental feeding, which can be challenging because an incorrect balance between fresh grass allowance and feed supplementation results in inefficient use of the pasture, lower feed efficiency, and potential decreases in animal production. When estimating fresh grass allowance, we currently do not correct for the formation of rejected patches (RP) surrounding excreta, which can lead to overestimation of the potential fresh grass intake and hampers optimal grazing. In this study, therefore, we aim to quantify the formation of RP in intensive grazing systems and improve the quantification of fresh grass allowance. To do so, we studied 2 grazing systems (i.e., compartmented continuous grazing and strip grazing) that differ in key grazing characteristics, such as pre- and post-grazing heights and period of regrowth. The experiment was performed from April to October in 2016 and 2017 with 60 dairy cows at a fixed stocking rate of 7.5 cows/ha. Average pre-grazing grass height was measured with a rising plate meter. To quantify the formation of RP after grazing, individual grass height measurements were conducted after grazing and classified as RP or not, based on visual assessment. Our analysis showed that the average percentage of grassland covered with RP increased from around 22% at the end of May to around 43% at the end of July/beginning of August, and these percentages do not differ across grazing systems. The percentage of grassland covered with RP should be subtracted from the total grazed area to better estimate true fresh grass allowance., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The rising plate meter (RPM) is used to measure grass height, which subsequently is used in a calibration equation to estimate herbage mass (HM), an important parameter for optimization of feed management in grazing systems. The RPM is placed on the sward and measures the resistance of the sward toward the plate, which depends not only on grass length, but also on sward structure. The accuracy of the calibration equation for the RPM to estimate HM across grazing systems, however, has not yet been evaluated. Therefore, our aim was to analyze the effect of intensive grazing systems on RPM calibration for perennial ryegrass pastures. To do so, we studied 2 grazing systems: compartmented continuous grazing (CCG) and strip grazing (SG), which differ in key grazing characteristics, such as pre- and post-grazing heights and period of regrowth, that may influence tiller density and vertical flexibility of the sward. The experiment was performed from April until October in 2016 and 2017 with 60 dairy cows, at a fixed stocking rate of 7.5 cows per hectare. To calibrate the RPM, 256 direct measurements of HM >4 cm (i.e., above stubble) were collected by cutting and weighing plots of grass for CCG and SG. Our main interest was in the HM above stubble because this is consumed by cows. Herbage mass <4 cm represents the stubble left after grazing. Differences in HM <4 cm may (partially) explain differences in HM >4 cm between the grazing systems. Therefore, HM <4 cm was additionally measured on 4 out of every 8 plots per grazing system by cutting out quadrats to 0 cm with an electric grass trimmer. Our results showed an average error margin in our calibration equations of 25 to 31%, expressed as the root mean square error of prediction (RMSEP) as a percentage of the observed HM >4 cm. Differences between grazing systems were relatively small, and including grazing system as a factor in the regression model to explain the increase in HM per centimeter of grass did not reduce the RMSEP of the model to any relevant extent. On the other hand, HM <4 cm was significantly greater on CCG compared with SG, with 2,042 kg of DM per hectare for CCG and 1,676 kg of DM per hectare for SG. The HM <4 cm, however, is not used for grazing, and this difference was not reflected in the HM >4 cm. Our results indicate that we can use one region-specific calibration equation for perennial ryegrass pastures across intensive grazing systems, despite relatively large differences in pre- and post-grazing heights and period of regrowth., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)