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Research Article| Volume 97, ISSUE 12, P7614-7632, December 2014

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Characterization of nitrogen, phosphorus, and potassium mass balances of dairy farms in New York State

Open ArchivePublished:October 25, 2014DOI:https://doi.org/10.3168/jds.2014-8467

      Abstract

      A whole-farm nutrient mass balance (NMB) is a useful measure of the nutrient status of a dairy farm. Research is needed to define and determine a feasible NMB range for dairy farm systems in New York State (NY). The objectives of this study were to (1) document the distribution of N, P, and K mass balances of 102 NY dairy farms (including 75 small, 15 medium, and 12 large farms); (2) establish initial NMB benchmarks based on what 75% of the farms achieved; (3) determine the maximum animal density that allows an example NY dairy farm to balance cow P excretions and crop P removal without exporting crops or manure; and (4) identify opportunities to improve NMB over time. Nutrient mass balances of the 102 farms ranged from −39 to 237 kg of N/ha for N without including N2 fixation (N1), from −14 to 259 kg of N/ha when N2 fixation was included (N2), from −7 to 51 kg of P/ha, and from −46 to 148 kg of K/ha. Seventy-five percent of the farms were operating at NMB less than 118 kg of N/ha for N1, 146 kg of N/ha for N2, 13 kg of P/ha, and 41 kg of K/ha (75% benchmarks). Farms with the highest nutrient use efficiencies (lowest NMB per unit of milk produced) operated with less than 8.8 kg of N/Mg of milk for N1, 11.8 kg of N/Mg of milk for N2, 1.1 kg of P/Mg of milk, and 3.0 kg of K/Mg of milk. The biggest contributor to the NMB was the amount of imported nutrients, primarily feed purchases. The example farm assessment (assuming no export of crops or manure) suggested that, when 70% of the feed is produced on the farm and P in feed rations does not exceed 4 g of P/kg of DM, cow P excretion and crop P removal were balanced at a maximum animal density of 2.4 animal units (AU)/ha (~0.97 AU/acre). Dairy farms operating with animal densities <2.4 AU/ha typically had NMB below the 75% benchmark, whereas most dairies with more than 2.4 AU/ha needed to export manure or crops to meet the 75% benchmark. Opportunities to reduce NMB on many farms, independent of size and without changes in animal density, are possible by more tightly managing fertilizer and feed imports, increasing the percentage of farm-produced nutrients, implementing precision feeding, and exporting crops or manure.

      Key words

      Introduction

      The long-term sustainability of dairy farms depends upon their ability to be profitable while limiting their environmental footprint. Over time, a general trend toward intensification of dairy farming has occurred (
      • Gourley C.J.P.
      • Powell J.M.
      • Dougherty W.J.
      • Weaver D.M.
      Nutrient budgeting as an approach to improving nutrient management on Australian dairy farms.
      ,
      • Gourley C.J.P.
      • Dougherty W.J.
      • Weaver D.M.
      • Aarons S.R.
      • Awty I.M.
      • Gibson D.M.
      • Hannah M.C.
      • Smith A.P.
      • Peverill K.I.
      Farm-scale nitrogen, phosphorus potassium and sulfur balances and use efficiencies on Australian dairy farms.
      ;
      USDA-NASS (National Agricultural Statistics Service)
      Overview of the United States dairy industry.
      ). Intensification (an increase in animal density) has the potential to increase nutrient imbalances and the risk of nutrient losses to the environment. However, appropriate resource management, including the export of crops or manure, can offset the risk. The development and implementation of tools and policies that address nutrient imbalances before they become extreme are essential to the long-term sustainability of dairy farming.
      In New York State (NY), dairy farming is the largest agricultural industry (
      USDA-NASS (National Agricultural Statistics Service)
      2007 Census of Agriculture. New York. State and County data. Volume 1. Geographic Area Series. Part 32.
      ). In 1999, NY introduced its first concentrated animal-feeding operation (CAFO) permit, developed under the Clean Water Act (
      USDA-EPA (Environmental Protection Agency)
      Unified national strategy for animal feeding operations.
      ). The permit designated 2 regulated groups: “medium” (200 to 699 cows) and “large” farms (>700 cows). For purposes of our study, we used this delineation to create 3 dairy size categories consisting of small (<200 cows), medium (200–699 cows), and large (700 or more cows). The 2007 USDA census shows 5,683 dairy farms in NY, including 5,092 small farms (90%) and 591 farms (10%) with more than 200 cows. Though USDA dairy cattle census categories do not follow the CAFO thresholds, a significant majority of dairy farms in NY fall into the small farm category used in the current study.
      Under the NY CAFO permit, all regulated dairy and livestock farms are required to develop and annually update a comprehensive nutrient-management plan (CNMP). Much of the CNMP is based on static and uniform guidelines and practices applied across all operations. The CNMP is presumed to be protective and compliance is achieved mainly by following the plan. Deviation from the plan is considered noncompliance even if no environmental harm was observed. More recently, the Natural Resources Conservation Service (NRCS) has begun to explore outcome-based approaches to incentivize and achieve improvements in whole-farm nutrient management over time through the introduction of the adaptive management concept (
      • Ketterings Q.M.
      Extension and knowledge transfer: Adaptive management approaches for timely impact.
      ;
      Natural Resources Conservation Service (NRCS)
      Adaptive Nutrient Management Process. Agronomy Technical Note No. 7.
      ). Adaptive management allows farms to set a performance base and then choose the range of practices and approaches to meet goals over time. Performance is evaluated based upon measured results.
      In a nutrient management context, the process of adaptive management involves characterizing, planning, evaluating, and adjusting management strategies over time (
      • Ketterings Q.M.
      Extension and knowledge transfer: Adaptive management approaches for timely impact.
      ;
      Natural Resources Conservation Service (NRCS)
      Adaptive Nutrient Management Process. Agronomy Technical Note No. 7.
      ). This requires the use of tools to both determine a starting point (current status or base) and to evaluate the effects of management changes over time. An annual whole-farm nutrient mass balance (NMB) assessment is one of very few on-farm tools that facilitate the adaptive management approach at the whole-farm level (
      • Ketterings Q.M.
      Extension and knowledge transfer: Adaptive management approaches for timely impact.
      ;
      • Soberon M.A.
      • Ketterings Q.M.
      • Rasmussen C.N.
      • Czymmek K.J.
      Whole-farm nutrient balance calculator for New York dairy farms.
      ). Whole-farm NMB data are often relatively easy to collect and the assessment results in a summary of large amounts of data in easy-to-understand input-output diagrams (
      • Oenema O.
      • Kros H.
      • de Vries W.
      Approaches and uncertainties in nutrient budgets: Implications for nutrient management and environmental policies.
      ).
      Several NMB calculators have been reported in the literature. The NMB calculator developed at Cornell University was used in our study; it determines the difference between the amount of nutrients imported onto the farm (in the form of feed, fertilizer, N2 fixation, animals, and bedding) and the amount of nutrients exported from the farm (as milk, animals, crops and manure, hereafter referred to as managed exports;
      • Klausner S.D.
      • Fox D.G.
      • Rasmussen C.N.
      • Pitt R.E.
      • Tylutki T.P.
      • Wright P.E.
      • Chase L.E.
      • Stone W.C.
      Improving dairy farm sustainability I: An approach to animal and crop nutrient management planning.
      ;
      • Spears R.A.
      • Kohn R.A.
      • Young A.J.
      Whole-farm nitrogen balance on western dairy farms.
      ,
      • Spears R.A.
      • Young A.J.
      • Kohn R.A.
      Whole-farm phosphorus balance on western dairy farms.
      ;
      • Soberon M.A.
      • Ketterings Q.M.
      • Rasmussen C.N.
      • Czymmek K.J.
      Whole-farm nutrient balance calculator for New York dairy farms.
      ). The results are expressed per tillable hectare, per animal unit (AU), and per megagram of milk production. All 3 outcome measures can be monitored over time through annual assessments.
      Though nutrient losses occur in every type of farming system, positive (surplus) NMB that result when nutrient imports exceed managed exports can be indicators of potential nutrient loss to the environment (
      • Koelsch R.K.
      • Lesoing G.
      Nutrient balance on Nebraska livestock confinement systems.
      ;
      • Oenema O.
      • Kros H.
      • de Vries W.
      Approaches and uncertainties in nutrient budgets: Implications for nutrient management and environmental policies.
      ;
      • Gourley C.J.P.
      • Powell J.M.
      • Dougherty W.J.
      • Weaver D.M.
      Nutrient budgeting as an approach to improving nutrient management on Australian dairy farms.
      ). Excess N can reflect additional ammonia volatilization to the atmosphere, nitrate leaching to ground water, or denitrification and greenhouse gas emissions (annual losses). Excess P can build up in the soil and, over time, contribute to P runoff, P leaching, and eutrophication of surface waters (
      • Spears R.A.
      • Young A.J.
      • Kohn R.A.
      Whole-farm phosphorus balance on western dairy farms.
      ). Excess K can also build up in the soil and lead to elevated K concentrations in forages, potentially affecting herd nutrition programs (
      • Fisher L.J.
      • Dinn N.
      • Tait R.M.
      • Shelford J.A.
      Effect of dietary potassium on the absorption and excretion of calcium and magnesium by lactating cows.
      ;
      • Cherney J.H.
      • Cherney D.J.R.
      • Bruulsema T.W.
      Potassium management.
      ). In the case of N, negative (deficient) balances resulting when managed exports exceed imports can have a negative effect on crop yield. In the case of P and K, negative balances can result in declining soil P and K levels over time (soil mining) and reduced crop yield when soil P and K become deficient. However, negative P and K balances can be desirable for a period of time when initial soil P and K contents are very high (
      • Oenema O.
      • Kros H.
      • de Vries W.
      Approaches and uncertainties in nutrient budgets: Implications for nutrient management and environmental policies.
      ). A negative whole-farm NMB reduces but does not eliminate the risk of nutrient losses to the environment, as the distribution of nutrients within a farm can be unbalanced, with some areas having high net surpluses and others having net deficits (
      • Gourley C.J.P.
      • Dougherty W.J.
      • Weaver D.M.
      • Aarons S.R.
      • Awty I.M.
      • Gibson D.M.
      • Hannah M.C.
      • Smith A.P.
      • Peverill K.I.
      Farm-scale nitrogen, phosphorus potassium and sulfur balances and use efficiencies on Australian dairy farms.
      ). Timing and method of manure or fertilizer applications, even when well distributed, can result in undesirable losses as well.
      Whereas surplus NMB are inevitable in modern dairy farming systems due to unavoidable inefficiencies in crop and animal metabolisms, very few reference levels or benchmark NMB have been identified against which nutrient balances for dairy farms under a certain set of climate and soil conditions may be evaluated (
      • Oenema O.
      • Kros H.
      • de Vries W.
      Approaches and uncertainties in nutrient budgets: Implications for nutrient management and environmental policies.
      ;
      • Nielsen A.H.
      • Kristensen I.S.
      Nitrogen and phosphorus surpluses on Danish dairy and pig farms in relation to farm characteristics.
      ). In the Netherlands, the Mineral Accounting System established permissible N and P surpluses at 300 kg of N/ha for grassland and 175 kg of N/ha for arable land (without including N2 fixation as an input) and 17.6 kg of P/ha (40 kg of P2O5) in 1998 to 1999. A stepwise adjustment was implemented to reduce the permissible surpluses over time to 140 to 180 kg of N/ha per year for grassland, to 60 to 100 kg of N/ha per year for arable land, and to 8.8 kg of P/ha (20 kg of P2O5) in 2003 (
      • Wright S.
      • Mallia C.
      The Dutch approach to the implementation of the nitrate directive: Explaining the inevitability of its failure.
      ). The Mineral Accounting System was designed as a tool that assessed N and P from fertilizer and manure combined, and that could provide an incentive for good nutrient management at the whole-farm level (
      • Oenema O.
      • Berentsen P.
      Manure policy and MINAS: Regulating nitrogen and phosphorus surpluses in agriculture of the Netherlands.
      ). This system was discontinued because it was not in accordance with the mandatory requirements of the European Union (EU) Nitrates Directive (
      • Wright S.
      • Mallia C.
      The Dutch approach to the implementation of the nitrate directive: Explaining the inevitability of its failure.
      ). The EU Nitrates Directive currently limits the amount of N from organic sources that can be applied to land to 170 kg of N/ha per year (

      European Union. 1991. Council directive 91/676/EEC of 12 December Concerning the Protection of Waters Against Pollution Caused by Nitrates from Agricultural Sources. Off. J. Eur. Communities: L375. Brussels, Belgium.

      ). National governments can ask for derogation and permit higher organic N application rates under certain conditions if they demonstrate no adverse effect on water quality (
      • Van der Straeten B.
      • Buysse J.
      • Nolte S.
      • Lauwers L.
      • Claeys D.
      • Van Huylenbroeck G.
      The effect of EU derogation strategies on the compliance costs of the nitrate directive.
      ). Although there is no European directive regarding P application in agriculture, some European countries and regions restrict P fertilization via national or regional legislation (
      • Amery F.
      • Schoumans O.F.
      Agricultural phosphorus legislation in Europe.
      ). For instance, in Northern Ireland, dairy farms can increase their organic N loading limit to 250 kg of N/ha, but they must have a P balance (feed P + fertilizer P − milk P − animal P − crops P − manure P) below 10 kg of P/ha per year (
      Government of Northern Ireland
      Statutory rules of Northern Ireland. 2010 No. 411. Environmental protection, The Nitrates Action Programme Regulations (Northern Ireland) 2010.
      ). In Flanders,
      • Nevens F.
      • Verbruggen I.
      • Reheul D.
      • Hofman G.
      Farm gate nitrogen surpluses and nitrogen use efficiency of specialized dairy farms in Flanders: Evolution and future goals.
      set reasonable targets for sustainable dairy farming at 150 of kg N/ha (including N deposition and N2 fixation as inputs).
      • Nevens F.
      • Verbruggen I.
      • Reheul D.
      • Hofman G.
      Farm gate nitrogen surpluses and nitrogen use efficiency of specialized dairy farms in Flanders: Evolution and future goals.
      also stated that once dairy farms can produce milk within the reasonable target, the most efficient farms are those that operate with the lowest NMB per unit of milk produced, a measurement of nutrient use efficiency at the farm level. In New Zealand, dairy farmers conduct farm-based nutrient budgets using tools such as the Overseer nutrient budget model (
      • Gourley C.J.P.
      • Weaver D.M.
      Nutrient surpluses in Australian grazing systems: Management practices, policy approaches, and difficult choices to improve water quality.
      ), although no benchmark NMB data have been published to date.
      In the United States, a mineral accounting system or a whole-farm or field-level integrated nutrient balance approach has not previously been introduced as part of a regulatory framework, although several studies report whole-farm NMB. These studies included work with dairy farms in the northeast (
      • Lanyon L.E.
      • Beegle D.B.
      The role of on-farm nutrient balance assessments in an integrated approach to nutrient management.
      ;
      • Klausner S.D.
      Mass nutrient balances on dairy farms.
      ,
      • Klausner S.D.
      Nutrient management planning.
      ;
      • Klausner S.D.
      • Fox D.G.
      • Rasmussen C.N.
      • Pitt R.E.
      • Tylutki T.P.
      • Wright P.E.
      • Chase L.E.
      • Stone W.C.
      Improving dairy farm sustainability I: An approach to animal and crop nutrient management planning.
      ;
      • Anderson B.H.
      • Magdoff F.R.
      Dairy farm characteristics and managed flows of phosphorus.
      ;
      • Ketterings Q.M.
      • Czymmek K.J.
      • Beegle D.B.
      • Chase L.E.
      • Rasmussen C.N.
      Systematic nutrient (im)balances in dairy farm systems of the northeast and mid-Atlantic regions of the United States.
      ), in the Midwest (
      • Erb K.A.
      • Fermanich K.
      Nitrogen, phosphorus and potassium balances across dairy farm sizes: Do large dairies import more nutrients than small ones?.
      ), and in the west (
      • Spears R.A.
      • Kohn R.A.
      • Young A.J.
      Whole-farm nitrogen balance on western dairy farms.
      ,
      • Spears R.A.
      • Young A.J.
      • Kohn R.A.
      Whole-farm phosphorus balance on western dairy farms.
      ;
      • Hristov A.N.
      • Hazen W.
      • Ellsworth J.W.
      Efficiency of use of imported nitrogen, phosphorus, and potassium and potential for reducing P imports on Idaho dairy farms.
      ). None of these studies included the establishment of benchmark values or acceptable ranges.
      A target NMB should accommodate various production systems and objectives. It should simultaneously allow for the achievement of high milk production while minimizing surplus nutrients, thereby reducing the risk of nutrient losses to the environment. One way to proceed is to set NMB benchmarks at which a portion of the dairy farms within a region can operate across a variety of farming systems. Currently, such whole-farm NMB benchmarks do not exist for NY dairies or dairies in the rest of the United States. The objectives of our study were to (1) document the distribution of N, P, and K mass balances of several commercial dairies in NY in a given year (2006); (2) establish initial NMB benchmarks based on what 75% of the farms achieved; (3) determine the maximum animal density that allows an example NY dairy farm to balance cow P excretions and crop P removal without exporting crops or manure; and (4) identify opportunities to improve NMB over time.

      Materials and Methods

      Whole-Farm NMB

      Participating Dairies

      Dairy farmers were selected based upon their willingness to participate and the availability of adequate records to complete the mass balance assessment. Different channels were used to approach farmers: extension presentations, mailing, and personal contact with extension educators, NRCS staff, Soil and Water Conservation District staff, private sector nutrient management planners, and so on. Participation of farmers of all 3 size categories (small, medium, and large) was ensured. The assessment is typically conducted more efficiently on farms with good record-keeping systems (
      • Soberon M.A.
      • Ketterings Q.M.
      • Rasmussen C.N.
      • Czymmek K.J.
      Whole-farm nutrient balance calculator for New York dairy farms.
      ) and participating farms were selected with this in mind. Thus, the data set may not reflect a precise cross-section of the NY dairy industry and may be skewed toward managers with better record keeping. Results should be regarded as NMB that farms in a specific size class can achieve, rather than as an accurate representation of the entire NY dairy industry.
      Nutrient mass balances were calculated for 102 dairy farms in NY for the 2006 calendar year. Using current regulatory categories in NY (
      USDA-EPA (Environmental Protection Agency)
      Unified national strategy for animal feeding operations.
      ), farms were separated into 3 groups according to the number of milk cows: 75 farms were designated as small, 15 farms were considered medium, and 12 farms were labeled large. The study database is skewed toward medium and large farms, as it represents 1.5% of all small dairy farms and 4.6% of all medium and large dairy farms in NY in 2007.
      The 102 farms were located in 26 different counties and in 11 different NY watersheds, including the watersheds of the Allegheny River (n = 2), the Black River (n = 3), the Chemung River (n = 15), the Delaware River (n = 7), the Genesee River (n = 2), Lake Champlain (n = 10), Lake Erie-Niagara River (n = 1), Lake Ontario (n = 1), the Seneca-Oneida-Oswego Rivers (n = 13), the St. Lawrence River (n = 9), and the Susquehanna River (n = 39). Although prior NMB had been collected by 78 of the farms, 2006 was the single year with the largest number of farms surveyed (102), allowing a comparison of NMB under similar seasonal conditions for the largest number of farms and widest range of farm sizes in our database. The year 2006 was slightly wetter and warmer, with slightly lower milk prices and slightly higher crop prices compared with average annual values for NY. Total rainfall and average temperature in NY were 1,165 mm and 9.7°C, compared with normal annual averages (1970–2000) for NY of 990 mm and 8.1°C, respectively (
      New York Agricultural Statistics Service (NYSASS)
      New York Agricultural Statistics 2006–2007 Annual Bulletin.
      ). Milk prices received were $13.4 per 45.4 kg of milk, compared with the average price received in the previous 10 yr (1997–2006) of $14.4 ± 1.5 per 45.4 kg of milk. Corn silage prices averaged $18/t compared with the average marketing price of $16.1 ± 1.4/t for 1997 to 2006 (
      New York Agricultural Statistics Service (NYSASS)
      New York Agricultural Statistics 2006–2007 Annual Bulletin.
      ).
      Farms in the present study varied in size and management practices (Table 1). On average, the farms had 270 cows, 261 tillable ha, 1.67 AU/ha, produced 75% of the feed on farm, and generated 8,905 kg of milk/cow per year (Table 1). In comparison, the 5,683 dairy farms in NY in 2007 averaged 110 milk cows per farm (
      USDA-NASS (National Agricultural Statistics Service)
      2007 Census of Agriculture. New York. State and County data. Volume 1. Geographic Area Series. Part 32.
      ) and produced 8,563 kg of milk/cow per year in 2006 (
      USDA-NASS (National Agricultural Statistics Service)
      Milk production historic data.
      ). In addition to having more cows, medium and large farms also had more tillable hectares, higher milk production per cow and per hectare, and higher animal densities than small farms (Table 1). On average, across all farms, 39% of the tillable hectares were planted with corn, 57% were planted with hay, and 4% with other crops. This distribution is consistent with the 40 and 57% of harvested land in corn and hay-plus-haylage, respectively, reported for dairy farms in NY (
      USDA-NASS (National Agricultural Statistics Service)
      2007 Census of Agriculture. New York. State and County data. Volume 1. Geographic Area Series. Part 32.
      ).
      Table 1Descriptive statistics of selected characteristics for the 102 dairy farms surveyed in 2006 in New York State
      CharacteristicAll farms
      Q1, Q2, Q3, and Q4 indicate quartiles 1, 2, 3, and 4 respectively. Quartile 2=median. Quartile 4=maximum.
      (n = 102)
      Small (n = 75)Medium (n = 15)Large (n = 12)
      MeanSDMinimumQ1Q2Q3Q4MedianIQR
      IQR=interquartile range (Q3 − Q1).
      MedianIQRMedianIQR
      Farm size
       Milk cows (n)27041724631022182,30082
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      56319
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1511,060
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      621
       Animal units
      Animal unit equals 454kg (1,000 lbs) of animal weight (cows, heifers, calves).
      (AU)
      507762431111963933,572153
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      104637
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      3702,075
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      839
       Tillable land (ha)26129721921392751,619113
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      74312
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      161979
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      250
       Manured land (ha)185256749891671,41669
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      57243
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      144769
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      433
       Legume land
      Legume land=crop land with at least 10% of legumes.
      (ha)
      85141016417297124
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      37101
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      76334
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      172
      Herd management
       Milk sold (kg/cow per year)8,9051,9133,4927,7679,07210,04313,0018,737
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2,0119,952
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1,35910,683
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1,590
       Farm-produced feed (%)7512356776849777
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1668
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1171
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      13
       Farm-produced forage (%)7013346470789673156886310
       CP in ration (%)15.62.28.514.315.717.021.515.7
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2.915.5
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2.916.8
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2.2
       P in ration (%)0.510.150.270.420.480.560.990.46
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.130.49
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.130.57
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.07
       K in ration (%)1.520.370.481.321.491.702.771.57
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.411.20
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.381.37
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.12
      Crop management (%)
       Area in legumes281901425399724
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2525
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2637
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      11
       Area with manure6625745678710061
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      3978
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2082
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      27
      Density
       Animal density (AU/ha)1.670.760.361.111.572.154.181.35
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.802.10
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.742.54
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.90
       Milk sold (kg/ha)8,2314,6171,1364,6897,22910,33923,0756,059
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2,01110,959
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1,35914,548
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1,590
      Feed use efficiency (%)
       N18671418223816.2
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      8.120.5
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      3.620.9
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      4.0
       P248111722284520.4
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      8.826.9
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      10.328.6
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      5.4
       K9436911237.5
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      4.211.3
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      3.111.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2.2
      a–c Means within a row with different letters differ (P < 0.05; Kruskal-Wallis test).
      1 Q1, Q2, Q3, and Q4 indicate quartiles 1, 2, 3, and 4 respectively. Quartile 2 = median. Quartile 4 = maximum.
      2 IQR = interquartile range (Q3 − Q1).
      3 Animal unit equals 454 kg (1,000 lbs) of animal weight (cows, heifers, calves).
      4 Legume land = crop land with at least 10% of legumes.

      Data Collection

      A data collection questionnaire was developed to aid the farmers in gathering and reporting N, P, and K import and export data. Farmers were visited at least once, and follow-up phone calls and email communication took place with the farmers, their consultants (where applicable), and their nutritionists to complete the questionnaire and check data consistency and quality. Several farmer group meetings were held to explain the assessment process and ensure proper data collection. Assessments were based on farm records; no additional sample analyses were done. Data were collected in 2007 for the 2006 calendar year. The imports included purchased feed (corrected for inventories on January 1 and December 31), fertilizers, animals, and bedding, and their respective N, P, and K content. The managed exports included milk, animals, crops, and manure. Additional data collected included the number of milk cows, heifers, calves, and their BW, total farm land (owned plus rented land, including buildings and woodland), tillable land (owned and rented crop land and potentially tillable pasture), legume land (tillable land with >10% legumes), land receiving manure (tillable land that received manure either by mechanical spreading and or animal grazing), and the quantity and quality of farm-produced grains and forages.
      Where available, farm-specific laboratory analyses were used to determine forage nutrient content. When such analyses were not available, the long-term records of the Cornell Net Carbohydrate and Protein System (CNCPS) feed library were used (
      • Fox D.G.
      • Tedeschi L.O.
      • Tylutki T.P.
      • Russell J.B.
      • Van Amburgh M.E.
      • Chase L.E.
      • Pell A.N.
      • Overton T.R.
      The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion.
      ). This feed library is based upon data reported by
      NRC
      Nutrient Requirements of Beef Cattle.
      ,
      • Sniffen C.J.
      • O’Connor J.D.
      • Van Soest P.J.
      • Fox D.G.
      • Russell J.B.
      A net carbohydrate and protein system for evaluating cattle diets. II. Carbohydrate and protein availability.
      , and
      • Van Soest P.J.
      Nutritional Ecology of the Ruminant.
      . The body composition of dairy and beef cattle purchased and sold was set at 2.40 to 2.90, 0.65 to 0.73, and 0.20% of BW for N, P, and K, respectively, derived from
      • Maynard L.A.
      • Loosli J.K.
      • Hintz H.F.
      • Warner R.G.
      Animal nutrition.
      and
      NRC
      Nutrient Requirements of Beef Cattle.
      ,
      NRC
      Nutrient Requirements of Dairy Cattle.
      ). These values are similar to those used in previous NMB studies (2.53 to 2.88% for N, 0.70 to 0.75% for P, and 0.18 to 0.30% for K;
      • Anderson B.H.
      • Magdoff F.R.
      Dairy farm characteristics and managed flows of phosphorus.
      ;
      • Hristov A.N.
      • Hazen W.
      • Ellsworth J.W.
      Efficiency of use of imported nitrogen, phosphorus, and potassium and potential for reducing P imports on Idaho dairy farms.
      ;
      • Gourley C.J.P.
      • Dougherty W.J.
      • Weaver D.M.
      • Aarons S.R.
      • Awty I.M.
      • Gibson D.M.
      • Hannah M.C.
      • Smith A.P.
      • Peverill K.I.
      Farm-scale nitrogen, phosphorus potassium and sulfur balances and use efficiencies on Australian dairy farms.
      ;
      • Plaizier J.C.
      • Legesse G.
      • Ominski K.H.
      • Flaten D.
      Whole-farm budgets of phosphorus and potassium on dairy farms in Manitoba.
      ). Milk protein reported to producers as true protein was converted to CP by multiplying by 1.075, based upon ranges found in the literature (
      • DePeters E.J.
      • Ferguson J.D.
      Nonprotein nitrogen and protein distribution in the milk of cows.
      ), and divided by 6.38 to obtain N concentration in the milk (
      • Higgs R.J.
      • Chase L.E.
      • Van Amburgh M.E.
      Development and evaluation of equations in the Cornell Net Carbohydrate and Protein System to predict nitrogen excretion in lactating dairy cows.
      ). As the milk P and K concentrations are not reported on milk quality reports received by producers, this assessment used 0.09% for P and 0.16% for K based on work by
      • Knowlton K.F.
      • Herbein J.H.
      Phosphorus partitioning during early lactation in dairy cows fed diets varying in phosphorus content.
      and
      • Fisher L.J.
      • Dinn N.
      • Tait R.M.
      • Shelford J.A.
      Effect of dietary potassium on the absorption and excretion of calcium and magnesium by lactating cows.
      , respectively.

      Calculations

      The Cornell University NMB calculator described in
      • Soberon M.A.
      • Ketterings Q.M.
      • Rasmussen C.N.
      • Czymmek K.J.
      Whole-farm nutrient balance calculator for New York dairy farms.
      was used to calculate the whole farm NMB for each of the 102 farms. For each farm, an NMB was calculated by summing the nutrients in imported feed, fertilizers, animals, and bedding, and subtracting exported nutrients in milk, animals, crops, and manure. The NMB were expressed per tillable hectare (defined as owned and rented crop land and potentially tillable pastured land, which represents a gross measure of how well farms are recycling nutrients on their land base), or per total milk production or AU (nutrient use efficiency indicators). The K mass balances per total milk production and per AU presented 1 outlier (data input related) among the 102 farms; this farm balance was removed from the analysis.

      Uncertainty in Nutrient Balances

      The whole-farm NMB generally has smaller uncertainties than other types of nutrient balances (
      • Oenema O.
      • Kros H.
      • de Vries W.
      Approaches and uncertainties in nutrient budgets: Implications for nutrient management and environmental policies.
      ). For example,
      • Gourley C.J.P.
      • Dougherty W.J.
      • Weaver D.M.
      • Aarons S.R.
      • Awty I.M.
      • Gibson D.M.
      • Hannah M.C.
      • Smith A.P.
      • Peverill K.I.
      Farm-scale nitrogen, phosphorus potassium and sulfur balances and use efficiencies on Australian dairy farms.
      reported that the integrated uncertainty of the whole-farm NMB was relatively small (median 4%). In a survey conducted among students and producers that conducted NMB in NY, participants identified nutrient composition of purchased bedding, crops produced on the farm, and tillable area receiving manure as the most uncertain data, whereas the greatest certainty was placed on farm records for feed imports, milk sales, and animal number (
      • Soberon M.A.
      • Ketterings Q.M.
      • Rasmussen C.N.
      • Czymmek K.J.
      Whole-farm nutrient balance calculator for New York dairy farms.
      ). The uncertainty in the main drivers of nutrient imports (feed and fertilizer) and exports (milk) is typically low, because these are purchased inputs for which farmers keep sales records for tax-reporting purposes. The highest uncertainties could be expected in the information used to derive efficiency indicators, as those indicators include estimates of on-farm forage production (
      • Soberon M.A.
      • Ketterings Q.M.
      • Rasmussen C.N.
      • Czymmek K.J.
      Whole-farm nutrient balance calculator for New York dairy farms.
      ).
      Previous studies have identified N2 fixation of legumes as the import with the highest degree of uncertainty (
      • Oenema O.
      • Kros H.
      • de Vries W.
      Approaches and uncertainties in nutrient budgets: Implications for nutrient management and environmental policies.
      ,
      • Gourley C.J.P.
      • Dougherty W.J.
      • Weaver D.M.
      • Aarons S.R.
      • Awty I.M.
      • Gibson D.M.
      • Hannah M.C.
      • Smith A.P.
      • Peverill K.I.
      Farm-scale nitrogen, phosphorus potassium and sulfur balances and use efficiencies on Australian dairy farms.
      ). In NY, estimating the actual rate of N2 fixation for any given legume population is challenging due to (1) the uncertainty in estimating the legume fraction of mixed hay stands (most stands are a combination of grass and alfalfa), (2) the uncertainty in estimating the percentage of legume land receiving manure, and (3) limited understanding and quantification of N2 fixation levels under various field conditions (with and without manure addition, high versus low OM levels, and so on). Comparisons of individual NMB can be affected by methods used to account for N2 fixation. For this reason, 2 N balances are presented in the current study: an N balance without N2 fixation (N1) and an N balance that assumes a yield-specific N2 fixation that separates fields in >90% legume (pure seedings) and a more typical (for NY) 60% legume/grass mixture (N2). For legume crops or pastures with >90% legume (pure seedings), atmospheric N2 fixation by legumes was set at 60% of the N content of the forage. For each legume crop or pasture with <90% legumes, a more typical (for NY) 60% legume and grass mixture was assumed, therefore, N2 fixation accounted for 36% of the N content of the forage. These are conservative N2 fixation figures that assume no manure application to legume crops based upon ranges reported by
      • Carlsson G.
      • Huss-Danell K.
      Nitrogen fixation in perennial forage legumes in the field.
      .
      Other potential sources of N inputs include atmospheric deposition and irrigation water. Statewide, atmospheric N deposition is estimated at 9 kg of N/ha per year (
      • Baumgardner Jr., R.E.
      • Lavery T.F.
      • Rogers C.M.
      • Isil S.S.
      Estimates of atmospheric deposition of sulfur and nitrogen species: Clear air status and trends network, 1990–2000.
      ). However, atmospheric N deposition and N in irrigation water were not considered in the NMB calculation in our study because farmers cannot manage N deposition and irrigation is rarely used on NY dairy farms, representing approximately 0.17% of the harvested cropland (
      USDA-NASS (National Agricultural Statistics Service)
      2007 Census of Agriculture. New York. State and County data. Volume 1. Geographic Area Series. Part 32.
      ).
      Additional farm characteristics reported for the farms included (equations 1 through 8):
      • (1)
        AU = [sum of (number of animals for each animal group × average weight in kilograms for each animal group)]/454 kg;
      • (2)
        All feed (forage and concentrates; kg of DM) = purchased feed + farm-produced feed;
      • (3)
        All nutrients in feed (kg of DM) = purchased feed × nutrient concentration + farm-produced feed × nutrient concentration;
      • (4)
        Farm-produced feed (kg of DM) = farm crop production + initial inventory − final inventory − crops sold;
      • (5)
        Farm-produced nutrients (kg of DM) = farm crop production × nutrient concentration + (initial inventory − final inventory) × nutrient concentration − crops sold × nutrient concentration;
      • (6)
        Farm-produced feed (%) = (farm-produced feed/all feed) × 100;
      • (7)
        Farm-produced nutrients (%) = (farm-produced nutrients/all nutrients in feed) × 100; and
      • (8)
        Feed use efficiency (%) = (total nutrients sold in milk + animals)/all nutrients in feed.
      Descriptive statistics of the farm characteristics, NMB, imports, and managed exports were calculated for the whole database (102 farms) and for the 3 different farm-size categories. The variables were checked for normality (Proc Univariate;
      SAS Institute Inc
      SAS/STAT User's Guide Release 8.00.
      ). Most of the variables were not normally distributed. A nonparametric test (the Kruskal-Wallis one-way ANOVA) was performed to evaluate if the variables differed among the 3 farm sizes. Simple linear regression models were developed with the NMB regressed on the different types of nutrient imports and managed exports.
      For characterization purposes, NMB included all the imports and managed exports described previously. However, to better understand the relationships between NMB and farm size and management practices, NMB were also calculated without manure exports. This additional assessment was performed to evaluate the effect of manure export (a possible way to reduce balances) on NMB. In our database, 13 farms exported manure, and most (n = 8) of the farms that exported manure were classified as large farms. A stepwise regression (Proc Glmselect;
      SAS Institute Inc
      SAS/STAT User's Guide Release 8.00.
      ) was used to identify farm size characteristics and management practices that significantly (P = 0.05) explained the NMB without manure exports. Multivariate regression models (Proc Glm;
      SAS Institute Inc
      SAS/STAT User's Guide Release 8.00.
      ) were developed with NMB without manure exports as the dependent variables, and the significant farm size characteristics and management practices as independent variables. Linear regressions were performed with NMB without manure exports as the dependent variable and animal density as the independent variable.

      Animal Densities to Balance P in an Example NY Dairy Farm

      Most NY dairy producers compose cattle rations using home-grown forage for a significant portion of the diet. Due to adequate natural rainfall and good management, forage yields in the northeast are relatively reliable and, if harvested at the optimal time, of high quality. The forage generally consists of corn (Zea mays L.) for silage and various combinations of grass and legume species, such as alfalfa (Medicago sativa L.), for hay crop silage or dry hay. Most dairy farms purchase concentrates (grain and other products), although some dairy farms also produce feed grains such as corn and small grains for grain, silage, or bedding. Following
      • Ketterings Q.M.
      • Czymmek K.J.
      • Beegle D.B.
      • Chase L.E.
      • Rasmussen C.N.
      Systematic nutrient (im)balances in dairy farm systems of the northeast and mid-Atlantic regions of the United States.
      , we defined an example dairy ration as (1) 70% DM from forage and 30% DM from concentrates (grain), such as corn or soybean (Glycine max L.) meal, and so on, and (2) 50% of the forage DM fed is corn silage versus 50% hay or hay crop silage. A moderately high-producing Holstein cow (29 kg milk/cow per day) weighing 658 kg was assumed to eat 20.9 to 21.3 kg of DM/d of this ration over the course of 12 mo. The CNCPS (
      • Fox D.G.
      • Tedeschi L.O.
      • Tylutki T.P.
      • Russell J.B.
      • Van Amburgh M.E.
      • Chase L.E.
      • Pell A.N.
      • Overton T.R.
      The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion.
      ) was used to determine P excretion assuming a percentage of P in the ration ranging from 4 to 6 g of P/kg. An extensive forage analysis library with over 237,000 corn silage samples and over 24,000 alfalfa or grass hay samples (
      DairyOne
      Statistical summary guidelines. DairyOne Feed Composition Library.
      ) was used to determine crop P content and the associated amounts of crop P removal. The assessment was performed for P to reflect a P-based approach to manure management. Due to typical N:P ratios in manure versus crop availability and uptakes, a P-based approach is more likely to limit manure application rates than an N-based approach when soil test P content is classified as optimum or higher (
      • Eghball B.
      • Power J.F.
      Phosphorus- and nitrogen-based manure and compost applications; corn production and soil phosphorus.
      ).

      Results and Discussion

      Whole-Farm NMB

      NMB per Hectare

      The 102 farms were operating with N1 balances ranging from −39 to 237 kg of N/ha (average = 74 kg of N/ha), N2 balances ranging from −14 to 259 kg of N/ha (average 105 kg of N/ha), P balances ranging from −7 to 51 kg of P/ha (average = 10 kg P of /ha), and K balances ranging from −46 to 148 kg of K/ha (average = 29 kg of K/ha; Table 2). Five to ten percent of the dairy farms exhibited negative NMB (<0) for 1 or more nutrients. Only 2 farms showed small negative N2 balances (−14 and −7 kg of N/ha) when N2 fixation was considered as an input. For P and K, negative NMB reflect mining of soil P and K over time. In farms with low initial soil test P and K, negative P and K balances can have a negative effect on current and future crop yields (
      • Ketterings Q.M.
      • Czymmek K.J.
      • Ristow P.
      • Rasmussen C.N.
      • Swink S.N.
      State, regional and farm-scale nutrient balances: Tools for enhanced efficiency of whole farm nutrient use.
      ). However, it should be recognized that farms can operate with negative P and K mass balances without affecting yield as long as soil test P and K levels are optimal or above (
      • Oenema O.
      • Kros H.
      • de Vries W.
      Approaches and uncertainties in nutrient budgets: Implications for nutrient management and environmental policies.
      ).
      Table 2Whole-farm nutrient mass balances (expressed as kg/ha, kg/Mg of milk, and kg/animal unit) and managed nutrient exports as a percentage of total nutrient imports, for 102 dairy farms in New York State in 2006
      CharacteristicNutrient
      The N1 balance does not include N2 fixation. The N2 balance includes N2 fixation estimates based on crop composition (% legume) only.
      All farms
      Q1, Q2, Q3, and Q4 indicate quartiles 1, 2, 3, and 4 respectively. Quartile 2=median. Quartile 4=maximum.
      (n = 102)
      Small (n = 75)Medium (n = 15)Large (n = 12)
      MeanSDMinimumQ1Q2Q3Q4MedianIQR
      IQR=interquartile range (Q3 – Q1).
      MedianIQRMedianIQR
      Balance (kg/ha)N17462−39286511823741
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      61104
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      43134
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      82
      N210565−14519514625976
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      78127
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      75175
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      82
      P108−73913517
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      910
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      712
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      10
      K2932−4610244114816
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2329
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2156
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      50
      Balance (kg/Mg of milk)N18.46.3−12.54.58.812.126.17.5
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      7.910.1
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      5.49.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      5.6
      N213.16.5−4.49.311.817.533.911.38.812.77.712.36.2
      P1.21.0−1.10.71.11.84.71.31.50.90.60.80.3
      K3.63.4−7.31.83.05.316.92.93.73.71.93.53.6
      Balance (kg/AU
      Animal unit (AU) equals 454kg (1,000 lbs) of animal weight (cows, heifers, calves).
      )
      N140.429.5−58.620.044.861.497.033
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      3953
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1851
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      24
      N260.928.4−20.640.458.480.9146.254
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      4474
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2769
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      26.9
      P5.74.3−6.43.15.48.718.55.56.55.62.94.71.4
      K16.515.0−36.38.315.125.057.6131615102317
      Exports/imports (%)N159681834446265054
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      3464
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1456
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      20
      N2381715273543125351531123516
      P61571839516656052314815417
      K58739293960533603867115824
      a,b Means within a row with different letters differ (P < 0.05; Kruskal-Wallis test).
      1 The N1 balance does not include N2 fixation. The N2 balance includes N2 fixation estimates based on crop composition (% legume) only.
      2 Q1, Q2, Q3, and Q4 indicate quartiles 1, 2, 3, and 4 respectively. Quartile 2 = median. Quartile 4 = maximum.
      3 IQR = interquartile range (Q3 – Q1).
      4 Animal unit (AU) equals 454 kg (1,000 lbs) of animal weight (cows, heifers, calves).
      Of the study farms, 75% were operating with NMB equal to or lower than 118 kg of surplus N/ha for N1, 146 kg of N/ha for N2, 13 kg of surplus P/ha, and 41 kg of surplus K/ha (quartile 3, Table 2). Significant differences were observed in NMB per hectare among the 3 different farm size categories, with large farms having larger median N1, N2, P, and K balances than small farms (Table 2). However, a large range in NMB was noted within each farm size, as shown by the interquartile ranges in Table 2, indicating that low and high NMB can be found regardless of farm size.
      In general, the ranges in NMB per hectare in our database were similar to the 97 to 270 kg of N/ha, 1 to 57 kg of P/ha, and 2 to 90 kg of K/ha ranges reported in the literature by
      • Lanyon L.E.
      • Beegle D.B.
      The role of on-farm nutrient balance assessments in an integrated approach to nutrient management.
      ,
      • Klausner S.D.
      • Fox D.G.
      • Rasmussen C.N.
      • Pitt R.E.
      • Tylutki T.P.
      • Wright P.E.
      • Chase L.E.
      • Stone W.C.
      Improving dairy farm sustainability I: An approach to animal and crop nutrient management planning.
      ,
      • Anderson B.H.
      • Magdoff F.R.
      Dairy farm characteristics and managed flows of phosphorus.
      ,
      • Erb K.A.
      • Fermanich K.
      Nitrogen, phosphorus and potassium balances across dairy farm sizes: Do large dairies import more nutrients than small ones?.
      , and
      • Plaizier J.C.
      • Legesse G.
      • Ominski K.H.
      • Flaten D.
      Whole-farm budgets of phosphorus and potassium on dairy farms in Manitoba.
      . The NMB ranges for European farms [106 to 609 kg of N/ha, 6 to 44 kg of P/ha, and 52 to 107 kg of K/ha based on
      • Nielsen A.H.
      • Kristensen I.S.
      Nitrogen and phosphorus surpluses on Danish dairy and pig farms in relation to farm characteristics.
      ,
      • Bassanino M.
      • Grigani C.
      • Sacco D.
      • Allisiardi E.
      Nitrogen balances at the crop and farm gate scale in livestock farms in Italy.
      , and
      • Fangueiro D.
      • Pereira J.
      • Coutinho J.
      • Moreira N.
      • Trindade H.
      NPK farm-gate nutrient balances in dairy farms from Northwest Portugal.
      ] and Australian farms [15 to 601 kg of N/ha, −7 to 133 kg of P/ha, and 13 to 452 kg of K/ha based on
      • Gourley C.J.P.
      • Powell J.M.
      • Dougherty W.J.
      • Weaver D.M.
      Nutrient budgeting as an approach to improving nutrient management on Australian dairy farms.
      ,
      • Gourley C.J.P.
      • Dougherty W.J.
      • Weaver D.M.
      • Aarons S.R.
      • Awty I.M.
      • Gibson D.M.
      • Hannah M.C.
      • Smith A.P.
      • Peverill K.I.
      Farm-scale nitrogen, phosphorus potassium and sulfur balances and use efficiencies on Australian dairy farms.
      )] were substantially higher than the ranges in NY, reflecting higher animal densities or greater fertilizer use.

      NMB per Megagram of Milk Produced

      In 2006, the farms were operating with NMB ranging from −12.5 to 26.1 kg of N/Mg of milk for N1, from −4.4 to 33.9 kg of N/Mg of milk for N2, from −1.1 to 4.7 kg of P/Mg of milk, and from −7.3 to 16.9 kg of K/Mg of milk (Table 2). Fifty percent of the farms were producing milk with less than 8.8 kg of surplus N for N1, 11.8 kg of surplus N for N2, 1.1 kg of surplus P, and 3.0 kg of surplus K per megagram of milk produced (quartile 2, Table 2). Farms with the lowest nutrient use efficiencies (quartile 4, Table 2) produced 38, 29, 213, and 59 kg of milk/kg of surplus N1, N2, P, and K, respectively. In comparison, the 50% of the farms in NY with the highest nutrient use efficiencies (quartile 2, Table 2) produced at least 114, 85, 909, and 333 kg of milk/kg of surplus N1, N2, P, and K, respectively.
      The N1 balances per megagram milk produced differed among farm sizes, with small farms operating, on average, with lower N surpluses per megagram milk than medium farms (Table 2). However, the N2, P, and K mass balances per megagram of milk production did not vary among farm sizes. These results suggest that large farms were not necessarily more or less efficient in terms of nutrient use than small farms. The ranges of NMB per megagram of milk production obtained in NY dairy farms in 2006 were similar to the 9 to 24 kg of N/Mg of milk (average = 17 kg of N/Mg of milk), 0.4 to 1.6 kg of P/Mg of milk (average = 1.2 kg of P/Mg of milk), and −7 to 16 kg of K/Mg of milk (average = 8 kg of K/Mg of milk) reported by
      • Hristov A.N.
      • Hazen W.
      • Ellsworth J.W.
      Efficiency of use of imported nitrogen, phosphorus, and potassium and potential for reducing P imports on Idaho dairy farms.
      for 6 intensive dairy farms in Idaho. The NY balances were lower than the 9 to 67 kg of surplus N/Mg of milk reported for more intensive dairy production systems of Flanders and The Netherlands (
      • Nevens F.
      • Verbruggen I.
      • Reheul D.
      • Hofman G.
      Farm gate nitrogen surpluses and nitrogen use efficiency of specialized dairy farms in Flanders: Evolution and future goals.
      ), and similar to or lower than the 9 to 45 kg of N/Mg of milk, −1 to 17 kg of P/Mg of milk, and 0 to 25 kg of K/Mg of milk ranges reported for 41 dairy farms in Australia (
      • Gourley C.J.P.
      • Dougherty W.J.
      • Weaver D.M.
      • Aarons S.R.
      • Awty I.M.
      • Gibson D.M.
      • Hannah M.C.
      • Smith A.P.
      • Peverill K.I.
      Farm-scale nitrogen, phosphorus potassium and sulfur balances and use efficiencies on Australian dairy farms.
      ), or 0 to 3.5 kg of P/Mg of milk, and 0 to 14 kg of K/Mg of milk range reported for 10 farms in Canada (
      • Plaizier J.C.
      • Legesse G.
      • Ominski K.H.
      • Flaten D.
      Whole-farm budgets of phosphorus and potassium on dairy farms in Manitoba.
      ).

      NMB per AU

      The NY study farms had NMB per AU that ranged from −59 to 97 kg of N/AU for N1, from −21 to 146 kg of N/AU for N2, from −6 to 19 kg of P/AU, and from −36 to 58 kg of K/AU (Table 2). Fifty percent of the farms were producing milk with less than 45 kg of surplus N for N1, 58 kg of surplus N per N2, 5 kg of surplus P, and 15 kg of surplus K per AU (quartile 2, Table 2). The median N1 and N2 surpluses per AU were lower on small farms than on large farms, whereas the P and K surplus per AU did not differ among farm sizes, consistent with data reported by
      • Klausner S.D.
      Nutrient management planning.
      .

      Nutrient Imports Accounted for in Managed Exports

      The average percentages of nutrients imported onto the 102 farms that were accounted for in managed exports were 59, 38, 61, and 58% for N1, N2, P, and K, respectively, with significant differences among farm sizes only for N1 (Table 2). These values are similar to or in the upper range of the 14 to 64% for N, 6 to 158% for P, and 9 to 157% for K reported in previous studies (
      • Lanyon L.E.
      • Beegle D.B.
      The role of on-farm nutrient balance assessments in an integrated approach to nutrient management.
      ;
      • Klausner S.D.
      Mass nutrient balances on dairy farms.
      ,
      • Klausner S.D.
      Nutrient management planning.
      ;
      • Klausner S.D.
      • Fox D.G.
      • Rasmussen C.N.
      • Pitt R.E.
      • Tylutki T.P.
      • Wright P.E.
      • Chase L.E.
      • Stone W.C.
      Improving dairy farm sustainability I: An approach to animal and crop nutrient management planning.
      ;
      • Spears R.A.
      • Kohn R.A.
      • Young A.J.
      Whole-farm nitrogen balance on western dairy farms.
      ,
      • Spears R.A.
      • Young A.J.
      • Kohn R.A.
      Whole-farm phosphorus balance on western dairy farms.
      ;
      • Hristov A.N.
      • Hazen W.
      • Ellsworth J.W.
      Efficiency of use of imported nitrogen, phosphorus, and potassium and potential for reducing P imports on Idaho dairy farms.
      ;
      • Gourley C.J.P.
      • Dougherty W.J.
      • Weaver D.M.
      • Aarons S.R.
      • Awty I.M.
      • Gibson D.M.
      • Hannah M.C.
      • Smith A.P.
      • Peverill K.I.
      Farm-scale nitrogen, phosphorus potassium and sulfur balances and use efficiencies on Australian dairy farms.
      ;
      • Plaizier J.C.
      • Legesse G.
      • Ominski K.H.
      • Flaten D.
      Whole-farm budgets of phosphorus and potassium on dairy farms in Manitoba.
      ).
      In our study, farms had similar milk production per cow (approximately 12 Mg of milk/cow per year) in each of the various NMB quartiles (from low to very-high balances), and milk production per cow and NMB were not correlated (Figure 1). This suggests opportunities for reducing NMB without a negative effect on milk production per cow. This is an area that deserves further exploration by dairy producers.
      Figure thumbnail gr1
      Figure 1Milk production per cow per year as a function of whole farm N, P, and K balances. For N, 2 mass balances were calculated: without N2 fixation (N1) and with N2 fixation assuming no manure application to legumes (N2).

      Nutrients Imported onto and Exported from Dairy Farms

      The total amount of nutrients imported varied widely among the 102 dairy farms in our database (Table 3). Imported feed was the single largest contributor to the total imports; on average, feed imports accounted for 74, 57, 73, and 66% of the total N1, N2, P, and K imports. Fertilizer imports were another important source of nutrient imports, accounting for 24, 19, 24, and 31% of the N1, N2, P, and K imports. Nitrogen fixation of legumes contributed, on average, 22% to the total N2 imports. Bedding and manure imports were negligible contributors to the overall balance for all farm sizes. Large dairy farms imported more N, P, and K via feed, fertilizer, N2 fixation, and animals than small farms (Table 3). The relative contribution of N2 fixation to the total N imports was slightly higher for small farms (average = 25%) than for medium and large farms (average = 16–17%). These findings for N are different from what is typically reported for European or Australian dairy farms (
      • Nevens F.
      • Verbruggen I.
      • Reheul D.
      • Hofman G.
      Farm gate nitrogen surpluses and nitrogen use efficiency of specialized dairy farms in Flanders: Evolution and future goals.
      ;
      • Gourley C.J.P.
      • Dougherty W.J.
      • Weaver D.M.
      • Aarons S.R.
      • Awty I.M.
      • Gibson D.M.
      • Hannah M.C.
      • Smith A.P.
      • Peverill K.I.
      Farm-scale nitrogen, phosphorus potassium and sulfur balances and use efficiencies on Australian dairy farms.
      ), where fertilizer is more often a large source of N imports, but they are consistent with previous NMB studies conducted with dairy farms in the United States (
      • Klausner S.D.
      Mass nutrient balances on dairy farms.
      ,
      • Klausner S.D.
      Nutrient management planning.
      ;
      • Klausner S.D.
      • Fox D.G.
      • Rasmussen C.N.
      • Pitt R.E.
      • Tylutki T.P.
      • Wright P.E.
      • Chase L.E.
      • Stone W.C.
      Improving dairy farm sustainability I: An approach to animal and crop nutrient management planning.
      ;
      • Spears R.A.
      • Kohn R.A.
      • Young A.J.
      Whole-farm nitrogen balance on western dairy farms.
      ,
      • Spears R.A.
      • Young A.J.
      • Kohn R.A.
      Whole-farm phosphorus balance on western dairy farms.
      ;
      • Hristov A.N.
      • Hazen W.
      • Ellsworth J.W.
      Efficiency of use of imported nitrogen, phosphorus, and potassium and potential for reducing P imports on Idaho dairy farms.
      ).
      Table 3Distribution of N, P, and K imported onto and exported from 102 dairy farms in New York State in 2006
      ItemNutrient
      The N1 balance does not include N2 fixation. The N2 balance includes N2 fixation estimates based on crop composition (% legume) only.
      All farms
      Q1, Q2, Q3, and Q4 indicate quartiles 1, 2, 3, and 4 respectively. Quartile 2=median. Quartile 4=maximum.
      (n = 102)
      Small (n = 75)Medium (n = 15)Large (n = 12)
      MeanSDMinimumQ1Q2Q3Q4MedianIQR
      IQR=interquartile range (Q3 – Q1).
      MedianIQRMedianIQR
      Imports, kg/ha
       TotalN11318626311618146593
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      82175
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      87272
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      116
      N2162912498147211520124
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      108193
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      114303
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      117
      P201101218247016
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1122
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1034
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      16
      K4836323406017935
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2654
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      22105
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      53
       FeedN99732498513540764
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      63134
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      96212
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      106
      P15110812206510
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1017
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1226
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      17
      K3128113233916819
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1935
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2845
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      34
       FertilizerN302509304410315
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      3139
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1952
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      42
      P44014717453456
      K15180211231007
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1713
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1729
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      59
       AnimalsN130000240.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.00.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1.10.3
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1.6
      P0.21000060.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.00.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.30.1
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.4
      K0.10.2000020.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.00.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.10.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.1
       MiscellaneousN260001440.10.50.21.00.83.2
      P0.320000230.00.00.00.10.10.4
      K270000580.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.00.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.00.1
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2.7
       N2 fixationN312201327429125
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      3121
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2649
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      22
      Exports, kg/ha
       TotalN5737835517026444
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      3066
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      43115
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      43
      P10616913358
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      512
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      819
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      8
      K201521016259114
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1119
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1236
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      26
       MilkN4324624385412132
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2256
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      3477
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      37
      P741479215
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      410
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      613
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      7
      K1372712173710
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      618
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1123
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      12
       AnimalN530346153.6
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2.95.1
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      2.911.9
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      6.0
      P11011140.9
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.71.2
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.72.9
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1.5
      K0.30.2000010.2
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.20.4
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.20.8
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.4
       CropN6130008850.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      8.70.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.44.9
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      6.2
      P120001120.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1.20.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.00.9
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1.7
      K5160005584.9
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      6.60.9
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.21.8
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      3.9
       ManureN31400001270.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.00.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.08.6
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      12.2
      P0.420000150.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.00.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.01.1
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      1.9
      K270000510.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.00.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      0.05.0
      Means within a row with different letters differ (P<0.05; Kruskal-Wallis test).
      8.0
      a–c Means within a row with different letters differ (P < 0.05; Kruskal-Wallis test).
      1 The N1 balance does not include N2 fixation. The N2 balance includes N2 fixation estimates based on crop composition (% legume) only.
      2 Q1, Q2, Q3, and Q4 indicate quartiles 1, 2, 3, and 4 respectively. Quartile 2 = median. Quartile 4 = maximum.
      3 IQR = interquartile range (Q3 – Q1).
      The managed exports varied greatly among the 102 dairy farms, with large farms exporting more nutrients per hectare than small farms (Table 3). Milk sales were the largest contributor to the total N, P, and K exports; on average, milk sales accounted for 76 to 78% of the total amounts exported. Crop and manure exports were also important drivers of the managed exports for some farms. Fifty percent of the farms exported at least some crops, without significant differences among farm sizes (Table 3). Thirteen percent of the farms exported manure, with large dairies exporting significantly more nutrients via manure than medium and small dairies (Table 3). This could be partly explained by the fact that large farms had, on average, higher animal densities than small or medium farms (Table 2), and partly because large farms tend to have larger manure storages. The manure export frequency is in agreement with those reported by
      • Ketterings Q.M.
      • Knight J.
      • Ristow P.
      • Swanpoel G.
      • Czymmek K.
      Evaluation of dairy and cash grain farmers’ perceptions of the value of manure.
      , who showed that the percentage of dairy farms in NY that exported manure increased with an increase in the animal density.

      75% NMB Benchmarks for Dairy Farms in NY

      Seventy-five percent of all farms in the database were operating with NMB less than 118 kg of N/ha for N1, 146 kg of N/ha for N2, 13 kg of P/ha, and 41 kg of K/ha (Table 2). The 75% benchmark approach for N1 (without considering N2 fixation) and P resulted in benchmarks similar to the permissible nutrient surpluses of 100 kg of N/ha and 9 kg of P/ha (20 kg of P2O5) set by the Dutch government for 2003 (
      • Wright S.
      • Mallia C.
      The Dutch approach to the implementation of the nitrate directive: Explaining the inevitability of its failure.
      ), and similar to the 10 kg of P/ha surplus required by Northern Ireland to request a derogation under the EU Nitrates Directive (
      Government of Northern Ireland
      Statutory rules of Northern Ireland. 2010 No. 411. Environmental protection, The Nitrates Action Programme Regulations (Northern Ireland) 2010.
      ). The 75% benchmark for N2 (with N2 fixation as an input) was similar to the 150 kg of N/ha value proposed for Flanders (
      • Nevens F.
      • Verbruggen I.
      • Reheul D.
      • Hofman G.
      Farm gate nitrogen surpluses and nitrogen use efficiency of specialized dairy farms in Flanders: Evolution and future goals.
      ).
      For N1, 83% of the small farms, 66% of the medium farms, and 42% of the large farms were operating with balances below 118 kg of N/ha. In comparison, for N2, 83% of small farms, 73% of the medium farms, and 16% of the large farms were operating with balances below 146 kg of N/ha. However, large farms typically applied manure to a larger percentage of hectares (Table 1), reflecting higher animal densities. If we assume that N2 fixation by legumes is reduced to 25% when manure is applied to all legume hectares (
      • Ketterings Q.M.
      • Frenay E.
      • Cherney J.H.
      • Czymmek K.J.
      • Klausner S.D.
      • Chase L.E.
      • Schukken Y.H.
      Application of manure to established stands of alfalfa and alfalfa-grass.
      ), the percentage of farms with N2 balances below 146 kg of N/ha increased to an estimated 93, 80, and 50% for small, medium, and large farms, respectively.
      Biological N2 fixation is difficult to quantify, often resulting in guesstimates (
      • Oenema O.
      • Kros H.
      • de Vries W.
      Approaches and uncertainties in nutrient budgets: Implications for nutrient management and environmental policies.
      ). Therefore, we reported N1 and N2 balances reflecting the minimum N balance (N1, without N2 fixation) and an estimate of the maximum N balance (N2), assuming no manure applied to legume hectares. Research is needed to improve the reliability of N2 fixation estimations and whole-farm N balance assessments for farms that grow legumes. Given the importance of legumes in crop rotations of dairies in NY, future mass balance assessments should include a reduction in N2 fixation in legume fields that received manure.
      For P, 79, 66, and 58% of small, medium, and large farms, respectively, operated with balances below 13 kg of P/ha, whereas for K, 84% of the small farms, 66% of the medium farms, and 42% of the large farms were operating with balances <41 kg of K/ha. The NMB benchmarks are conservative because they were obtained from a data set skewed toward medium and large farms that tended to have larger NMB than small farms (Table 1). If benchmarks were set based on what 75% of medium and large CAFO (both of which are currently regulated) were operating with, the 75% benchmarks in the current study would be 164 kg of N/ha for N1, 201 kg of N/ha for N2, 16 kg of P/ha, and 65 kg of K/ha.
      Operating with N and P mass balances below the 75% benchmark can help dairy farmers reduce the risk of N and P losses to the environment. Although K loss is not known to have environmental implications, monitoring of K balances is merited because of its nutritional effects and because it can help decrease the cost of production. Correlation between farm balances and K content in forages needs to be evaluated in future research in conjunction with soil test K trend evaluations.

      Optimal Zone for Milk Production

      In our study, 50% of the farms were operating with nutrient balances <8.8 kg of N/Mg of milk for N1, 11.8 kg of N/Mg of milk for N2, <1.1 kg of P/Mg of milk, and <3.0 kg of K/Mg of milk (Table 2, Figure 2). Following the approach outlined in
      • Nevens F.
      • Verbruggen I.
      • Reheul D.
      • Hofman G.
      Farm gate nitrogen surpluses and nitrogen use efficiency of specialized dairy farms in Flanders: Evolution and future goals.
      , an optimal zone for milk production (gray area in Figure 2) can be delimited by considering the farms operating below the 75% benchmark NMB per hectare and below the NMB per megagram of milk achieved by 50% of the farms. For N1, 55% of the small farms, 13% of the medium farms, and 33% of the large farms fell into the optimal zone in our study, versus 51% of the small farms, 33% of the medium farms, and 8% of the large farms for N2. For P, 44% of the small farms, 47% of the medium farms, and 58% of the large farms fell into the optimal zone. In comparison, 57% of the small farms, 47% of the medium farms, and 33% of the large farms operated in the optimal zone for K.
      Figure thumbnail gr2
      Figure 2Nitrogen, P, and K balances per hectare as a function of the animal density [animal units (AU)/ha] and the production intensity (Mg of milk/ha) for 102 dairy farms in New York State in 2006. White, gray, and black data points represent small (<200 cows), medium (200–699 cows), and large (700 or more cows) farms, respectively. Diamonds represent farms that did not export manure in 2006, whereas circles represent farms that exported manure in 2006. The gray area indicates the most efficient operational zone for efficient milk production. For N, 2 mass balances were calculated: without N2 fixation (N1) and with N2 fixation assuming no manure application to legumes (N2).
      For N, the percentage of farms that produced milk in the optimal zone was higher for small farms than for large farms, but the opposite trend was observed for P, possibly reflecting the emphasis on more precise management of P fertilizer and feed over time in NY (
      • Ketterings Q.M.
      • Czymmek K.J.
      Phosphorus index as a phosphorus awareness tool: documented phosphorus use reduction in New York State.
      ). Each of these ranges point out the potential for some farms to further improve on nutrient use efficiency over time.

      Maximum Animal Density to Balance P in an Example NY Dairy Farm

      The example ration defined in the current study (70% homegrown forages, 30% purchased concentrates) requires feeding approximately 5.4 Mg of forage DM to each cow annually (Table 4). Using the approach of
      • Ketterings Q.M.
      • Czymmek K.J.
      • Beegle D.B.
      • Chase L.E.
      • Rasmussen C.N.
      Systematic nutrient (im)balances in dairy farm systems of the northeast and mid-Atlantic regions of the United States.
      , if one assumes that 10% of the DM is lost in the process of mixing, feeding, and refusals, and that about 25% of the DM is lost between harvest and bunk silo storage, the producer must harvest about 8.0 Mg of forage DM, excluding safety margins for poor crop years. Summary data compiled by the National Agricultural Statistics Service indicate productive soils in the northeast and mid-Atlantic regions of the United States can average 16.8 to 17.9 Mg/ha of corn silage DM and 10.1 to 11.2 Mg/ha of DM of alfalfa or grass hay (
      • Ketterings Q.M.
      • Czymmek K.J.
      • Beegle D.B.
      • Chase L.E.
      • Rasmussen C.N.
      Systematic nutrient (im)balances in dairy farm systems of the northeast and mid-Atlantic regions of the United States.
      ). A dairy farm with a crop rotation of 4 yr in grass or alfalfa hay and 4 yr in corn silage would have half of its cropland in corn silage and the other half in hay. Given the yields above, an average of about 13.4 to 14.6 Mg of DM/ha would be produced across the crop fields and the rotation annually. Considering the estimates of forage DM needs, this productivity will support 1.7 to 1.8 cows/ha or about 2.4 to 2.6 AU/ha (0.95 to 1.05 AU/acre; Table 4).
      Table 4Production characteristics, cow ration, forage-based carrying capacity, crop P concentrations and uptake, and animal density to balance P in an example dairy farm in New York State
      Production characteristicMeasurement
      Milk production (kg/cow per day)29.3
      Cow BW (kg/cow)658
      Lactation period (d/yr)305
      Example cow ration
       DMI
        kg/cow per day21.1
        Mg/cow per year7.7
       Imported concentrate (% DMI)30
       Farm-produced corn silage (% DMI)35
       Farm-produced alfalfa/grass hay (% DMI)35
       Farm-grown forage needs (Mg/cow per year)5.4
       Feeding loss (% DM)10
       Harvest and storage loss (% DM)25
       Farm-grown forage production needs (Mg/cow per year)8.0
      Farm-produced forage yields and farm carrying capacity
       Corn silage (Mg/ha)16.8–17.9
       Alfalfa/grass hay (Mg/ha)10.1–11.2
       Rotation average (Mg/ha)13.4–14.6
      Farm-carrying capacity based on forage production
         milk cows/ha1.7–1.8
        AU
      Animal unit (AU) equals 454kg (1,000 lbs) of animal weight (cows, heifers, calves).
      /ha
      2.4–2.6
      Crop P concentrations and uptake
       Corn silage P concentration (%)0.24
       Alfalfa/grass hay P concentration (%)0.29
       Average crop rotation P uptake (kg of P/ha)36
      P excreted in urine and feces
       Ration P = 4 g/kg of DM
        g of P/cow per day60.0
        kg of P/cow per year21.9
       Ration P = 5 g/kg of DM
        g of P/cow per day82.0
        kg of P/cow per year29.9
       Ration P = 6 g/kg of DM
        g of P/cow per day103.0
        kg of P/cow per year37.6
      Animal density to balance P (excreted – crop uptake = 0)
       Ration P = 0.40% (AU/ha)2.4
       Ration P = 0.50% (AU/ha)1.8
       Ration P = 0.60% (AU/ha)1.4
      1 Animal unit (AU) equals 454 kg (1,000 lbs) of animal weight (cows, heifers, calves).
      Based on average P concentrations of 2.4 and 2.9 g of P/kg for corn silage and alfalfa or grass hay, respectively (
      DairyOne
      Statistical summary guidelines. DairyOne Feed Composition Library.
      ), 1.0 Mg of corn silage removes about 2.4 kg of P, and 1.0 Mg of alfalfa or grass removes about 2.9 kg of P. Based on the example corn silage and hay yields, this crop rotation removes on average 36 kg of P/ha (Table 4). The CNCPS (
      • Fox D.G.
      • Tedeschi L.O.
      • Tylutki T.P.
      • Russell J.B.
      • Van Amburgh M.E.
      • Chase L.E.
      • Pell A.N.
      • Overton T.R.
      The Cornell Net Carbohydrate and Protein System model for evaluating herd nutrition and nutrient excretion.
      ) estimates a total P excretion rate of 21.9 to 37.6 kg of P/cow per year for a 658-kg Holstein milking cow producing 29.3 kg of milk/cow per day and at feed P concentrations ranging from 4 to 6 g of P/kg (Table 4). Based on cow needs and the example rotation as summarized, the maximum animal densities that allow the example NY dairy farm to balance cow P excretions and crop P removal without exporting crops or manure ranges between 1.4 AU/ha (for rations with 6 g of P/kg of DM) to 2.4 AU/ha (for rations with 4 g of P/kg of DM; Table 4). Based on these calculations, at animal densities that exceed 2.4 AU/ha, P content in the ration exceeding 4 g of P/kg of DM or less than 70% home grown feed in the ration, manure or crop exports will need to be considered to prevent soil test P buildup over time.
      This is just one example of a possible NY dairy farm scenario. Several farm characteristics, such as soil type and landscape position, have an effect on possible rotations and related crop yields. Given that these characteristics vary across the state, the feasibility of a given farm to achieve the crop yields and percentages of homegrown feed illustrated in this example will vary depending on the farm resources and location. However, the 2.4 AU/ha (~0.97 AU/acre) animal density cutoff is consistent with the results of the 102-farm analysis, which indicated that farms operating with less than 2.4 AU/ha (~0.97 AU/acre) were typically able to meet the 75% benchmarks, whereas farms with >2.4 AU/ha needed to export manure or crops. Thus, both approaches, independently, suggest a maximum animal density of 2.4 AU/ha (~0.97 AU/acre) with the possibility to farm at higher densities if manure or crop exports can be managed.

      Management Options to Reduce NMB

      Animal Density

      Animal density was well correlated with the NMB per hectare when manure exports were disregarded in the assessment (Table 5, Figure 3). Increases in animal density corresponded with linear increases in milk production intensity (Mg of milk/ha), but also with linear increases in the NMB per hectare (Figure 3). In our database, 87% of the farms (95% of the small farms, 80% of the medium farms, and 50% of the large farms) were operating with animal densities below 2.4 AU/ha (~0.97 AU/acre; Table 4). Between 80 and 85% of the farms operating with animal densities below 2.4 AU/ha (without taking into account manure exports) achieved the 75% NMB per hectare benchmark. In turn, 73 to 87% of the dairy farms operating with animal densities higher than 2.4 AU/ha and not exporting manure exceeded the 75% NMB per hectare benchmark. A close relationship between animal density and NMB per hectare has also been reported in previous NMB studies (
      • Anderson B.H.
      • Magdoff F.R.
      Dairy farm characteristics and managed flows of phosphorus.
      ;
      • Gourley C.J.P.
      • Dougherty W.J.
      • Weaver D.M.
      • Aarons S.R.
      • Awty I.M.
      • Gibson D.M.
      • Hannah M.C.
      • Smith A.P.
      • Peverill K.I.
      Farm-scale nitrogen, phosphorus potassium and sulfur balances and use efficiencies on Australian dairy farms.
      ), further emphasizing the need to balance animal numbers with crop acreage to reduce the environmental footprint of dairy production.
      Table 5Multiple regressions with nutrient mass balance (without manure exports) expressed per hectare, per megagram of milk production, and per animal unit (AU), as a function of different farm size and management practices
      ParameterNitrogen (with N2 fixation)PhosphorusPotassium
      EstimateSEP-valueR2EstimateSEP-valueR2EstimateSEP-valueR2
      Mass balances per hectare (kg/ha)
       Intercept233.1530.4<0.0010.80131.634.66<0.0010.608109.2424.290.00010.660
       Animal density (AU/ha)47.965.25<0.0015.260.78<0.00117.082.85<0.001
       Feed use efficiency (%)−4.660.64<0.001−0.420.08<0.001−2.980.63<0.001
       Farm-produced nutrient (%)−2.200.31<0.001−0.330.05<0.001−1.120.24<0.001
       Total milk (kg/farm)4 × 10−69 × 10−7<0.001NSNS
       Land with legumes (%)45.6618.670.016NSNS
       Manured land (ha)NSNS0.0370.01<0.001
       Purchased forage (%)NSNS124.3536.270.001
      Mass balances per megagram of milk produced, (kg/Mg of milk)
       Intercept47.464.63<0.0010.4665.240.69<0.0010.40121.253.41<0.0010.440
       Feed use efficiency (%)−0.690.10<0.001−0.050.01<0.001−0.360.09<0.001
       Farm-produced nutrient (%)−0.280.04<0.001−0.040.01<0.001−0.180.03<0.001
       Milk per cow (lbs/cow year)−7 e−40.00030.0171 e−44 e−50.0124 e−42 e−40.016
       Land with legumes (%)10.172.64<0.001NSNS
       Nutrient in purchased feed (%)NS1.140.4080.006NS
       Nutrient in all feed (%)NSNS2.0690.890.022
       Manure land (ha)NSNS0.0030.0010.004
      Mass balances per animal unit (kg/AU)
       Intercept191.0417.91<0.0010.41621.182.75<0.0010.38386.4514.96<0.0010.428
       Feed use efficiency (%)−3.010.44<0.001−0.240.05<0.001−1.770.42<0.001
       Farm-produced nutrient (%)−1.220.18<0.001−0.190.03<0.001−0.860.12<0.001
       Tillable land (ha)0.030.0080.007NSNS
       Manure land (ha)NSNS0.020.01<0.001
       Nutrient in purchased feed (%)NS4.111.800.024NS
       Nutrients in all feed (%)NSNS8.443.970.036
      Figure thumbnail gr3
      Figure 3Nutrient mass balances per hectare (without manure export) and milk production intensity as a function of the animal density [animal units (AU)/ha] for 102 dairy farms in New York State in 2006. White, gray, and black diamonds represent small (<200 cows), medium (200–699 cows), and large (700 or more cows) farms, respectively. For N, 2 mass balances were calculated: without N2 fixation (N1) and with N2 fixation assuming no manure application to legumes (N2).
      Considerable variation in NMB per hectare at a given animal density was observed. Three farms with animal densities higher than 2.4 AU/ha managed N1 balances below the 75% benchmark, versus 2, 5, and 4 farms that operated below 75% benchmarks for N2, P, and K mass balances per hectare, respectively (Figure 2), suggesting that opportunities to reduce NMB exist without reducing the animal density.
      Animal densities in our NY study ranged from 0.36 to 4.18 AU/ha (median = 1.57 AU/ha; Table 2). These animal densities are similar to or lower than those reported in or calculated from other NMB studies conducted in the northeast United States (~0.7 to ~3.0 AU/ha;
      • Lanyon L.E.
      • Beegle D.B.
      The role of on-farm nutrient balance assessments in an integrated approach to nutrient management.
      ;
      • Klausner S.D.
      Nutrient management planning.
      ;
      • Anderson B.H.
      • Magdoff F.R.
      Dairy farm characteristics and managed flows of phosphorus.
      ) and Europe (~1.5 to ~8.0 AU/ha;
      • Nielsen A.H.
      • Kristensen I.S.
      Nitrogen and phosphorus surpluses on Danish dairy and pig farms in relation to farm characteristics.
      ;
      • Nevens F.
      • Verbruggen I.
      • Reheul D.
      • Hofman G.
      Farm gate nitrogen surpluses and nitrogen use efficiency of specialized dairy farms in Flanders: Evolution and future goals.
      ;
      • Bassanino M.
      • Grigani C.
      • Sacco D.
      • Allisiardi E.
      Nitrogen balances at the crop and farm gate scale in livestock farms in Italy.
      ;
      • Fangueiro D.
      • Pereira J.
      • Coutinho J.
      • Moreira N.
      • Trindade H.
      NPK farm-gate nutrient balances in dairy farms from Northwest Portugal.
      ).
      In general, NMB were better correlated with the total nutrient imports than with the managed nutrient exports (Table 6). In particular, study NMB were highly correlated with the feed nutrient imports. The close relationship between nutrient imports and NMB suggest that the biggest achievements in NMB can be made by reducing the amounts of imported nutrients, as reported by others (
      • Anderson B.H.
      • Magdoff F.R.
      Dairy farm characteristics and managed flows of phosphorus.
      ;
      • Hristov A.N.
      • Hazen W.
      • Ellsworth J.W.
      Efficiency of use of imported nitrogen, phosphorus, and potassium and potential for reducing P imports on Idaho dairy farms.
      ).
      Table 6Coefficients of determination of the linear regressions between the nutrient mass balance (per ha and per Mg of milk production) and the different types of nutrient imports and exports (expressed in the same units as the nutrient mass balance)
      Two N balances are presented: the balance without including N2 fixation (N1) and the balance with N2 fixation assuming no manure application to legumes (N2).
      ItemParameterMass balance (kg/ha)Mass balance (kg/Mg of milk)
      N1N2PKN1N2PK
      ImportsTotal0.867
      P<0.001
      0.842
      P<0.001
      0.751
      P<0.001
      0.831
      P<0.001
      0.766
      P<0.001
      0.446
      P<0.001
      0.702
      P<0.001
      0.784
      P<0.001
      Feed0.784
      P<0.001
      0.771
      P<0.001
      0.593
      P<0.001
      0.583
      P<0.001
      0.605
      P<0.001
      0.287
      P<0.001
      0.322
      P<0.001
      0.382
      P<0.001
      Fertilizer0.377
      P<0.001
      0.331
      P<0.001
      0.072
      P<0.01
      0.223
      P<0.001
      0.112
      P<0.001
      0.075
      P<0.01
      0.155
      P<0.001
      0.184
      P<0.001
      Animal0.113
      P<0.001
      0.122
      P<0.001
      0.053
      P<0.05
      0.054
      P<0.05
      0.090
      P<0.01
      0.078
      P<0.01
      0.055
      P<0.05
      0.034
      not significant, all at α=0.05.
      Miscellaneous0.001
      not significant, all at α=0.05.
      0.009
      not significant, all at α=0.05.
      0.083
      P<0.01
      0.122
      P<0.001
      0.018
      not significant, all at α=0.05.
      0.089
      P<0.01
      0.073
      P<0.01
      0.175
      P<0.001
      N2 fixation0.110
      P<0.001
      0.158
      P<0.001
      ExportsTotal0.259
      P<0.001
      0.329
      P<0.001
      0.079
      P<0.01
      0.006 ns0.196
      P<0.001
      0.108
      P<0.001
      0.148
      P<0.001
      0.134
      P<0.001
      Milk0.511
      P<0.001
      0.547
      P<0.001
      0.221
      P<0.001
      0.262
      P<0.001
      0.012
      not significant, all at α=0.05.
      0.065
      P<0.01
      0.001
      not significant, all at α=0.05.
      0.001
      not significant, all at α=0.05.
      Animal0.370
      P<0.001
      0.440
      P<0.001
      0.169
      P<0.001
      0.228
      P<0.001
      0.005
      not significant, all at α=0.05.
      0.013
      not significant, all at α=0.05.
      0.001
      not significant, all at α=0.05.
      0.011
      not significant, all at α=0.05.
      Crop0.099
      P<0.01
      0.062
      P<0.01
      0.094
      P<0.01
      0.113
      P<0.001
      0.207
      P<0.001
      0.123
      P<0.001
      0.134
      P<0.001
      0.122
      P<0.001
      Manure0.061
      P<0.05
      0.089
      P<0.01
      0.001
      not significant, all at α=0.05.
      0.011
      not significant, all at α=0.05.
      0.001
      not significant, all at α=0.05.
      0.004
      not significant, all at α=0.05.
      0.019
      not significant, all at α=0.05.
      0.006
      not significant, all at α=0.05.
      1 Two N balances are presented: the balance without including N2 fixation (N1) and the balance with N2 fixation assuming no manure application to legumes (N2).
      * P < 0.05
      ** P < 0.01
      *** P < 0.001
      not significant, all at α = 0.05.

      Fertilizer Imports

      One option to reduce nutrient imports is to reduce purchased fertilizer. On average, the farms in the 2006 database imported 30 kg of N/ha, 4 kg of P/ha, and 15 kg of K/ha in the form of fertilizer, representing 24, 19, 24, and 31% of total N1, N2, P, and K imports, respectively. In some cases, farmers can reduce fertilizer use through precision crop management without a negative effect on crop yields, as was shown for NY farms by
      • Ketterings Q.M.
      • Swink S.N.
      • Godwin G.
      • Czymmek K.J.
      • Albrecht G.L.
      Maize yield and quality response to starter phosphorus fertilizer in high phosphorus soils in New York.
      ) and
      • Ketterings Q.M.
      Extension and knowledge transfer: Adaptive management approaches for timely impact.
      .

      Feed Imports

      Dairy farms with the lowest NMB per hectare, per AU, and per megagram of milk produced tended to have the highest percentage of homegrown feed and nutrient, and the lowest percentage of imported nutrients (Table 5). An increase in homegrown feed can be achieved by increasing the land base, by increasing crop yields per unit land (e.g., through precision management), or by growing crops with higher nutrient content. Farms in our database produced from 35 to 97% of the feed (DM) at the farm (average = 75%). Only 9 of 102 farms were producing less than 60% of the feed on the farm, which is one of the benchmarks proposed for farmers who want to implement precision feed management in NY (
      • Cerosaletti P.
      • Dewing D.
      What is precision feed management?.
      ). None of the dairy farms produced all of their feed on the farm.

      Feed Use Efficiency

      Dairy farms can also reduce their NMB per hectare, per AU, and per megagram of milk produced by increasing feed use efficiency (Table 5). In our database, the average whole-farm feed use efficiencies were 18% for N, 24% for P, and 9% for K (Table 1). This feed use efficiency includes handling and storage losses, so feed use efficiencies at the cow level are higher. However, these values and the range in feed use efficiencies among farms suggest room for improvement on some farms. Study farms were feeding up to 215 g of CP/kg of DM, 9.9 g of P/kg of DM, and 27.7 g of K/kg of DM. Several studies have reported no improvements in milk and protein production when dietary CP is higher than 161 to 167 g of CP/kg of DM (
      • Colmenero J.J.
      • Broderick G.A.
      Effect of dietary crude protein concentration on milk production and nitrogen utilization in lactating dairy cows.
      ) and dietary P exceeds 3.8 g of P/kg of DM (
      • Powell J.M.
      • Wu Z.
      • Satter L.D.
      Dairy diet effects on phosphorus cycles of cropland.
      ). In our database, 31% of the dairy farms were feeding diets with more than 167 g of CP/kg of DM, and 84% of the dairy farms were feeding in excess of 3.8 g of P/kg of DM. This suggests that precision feed management could improve NMB on many dairy farms in our study while reducing the risk of environmental pollution without negatively affecting milk production (
      • Kohn R.A.
      • Dou Z.
      • Ferguson J.D.
      • Boston R.C.
      A sensitivity analysis of nitrogen losses from dairy farms.
      ;
      • Anderson B.H.
      • Magdoff F.R.
      Dairy farm characteristics and managed flows of phosphorus.
      ;
      • Powell J.M.
      • Wu Z.
      • Satter L.D.
      Dairy diet effects on phosphorus cycles of cropland.
      ;
      • Spears R.A.
      • Kohn R.A.
      • Young A.J.
      Whole-farm nitrogen balance on western dairy farms.
      ,
      • Spears R.A.
      • Young A.J.
      • Kohn R.A.
      Whole-farm phosphorus balance on western dairy farms.
      ;
      • Cerosaletti P.E.
      • Fox D.G.
      • Chase L.E.
      Phosphorus reduction through precision feeding of dairy cattle.
      ;
      • Hristov A.N.
      • Hazen W.
      • Ellsworth J.W.
      Efficiency of use of imported nitrogen, phosphorus, and potassium and potential for reducing P imports on Idaho dairy farms.
      ). Thus, the 75% benchmark balances for N, P, and possibly K may be adjusted over time as more farms implement precision feeding and fertilizer management and apply an adaptive management approach that continually adjust nutrient use over time.

      Crop and Manure Export

      Nutrient mass balances can be reduced by exporting crops and manure. This is illustrated by some farms that were operating at animal densities >2.4 AU/ha and yet had NMB below the 75% benchmark (Figure 2). Crop exports are a possibility for farms that already produce all the forage they need for the animals. This is feasible at lower animal densities but may not be an option when animal densities increase beyond 2.4 AU/ha, as illustrated herein. In our database, 50% of the farms exported crops, with an annual average export of 13 kg of N/ha, 2 kg of P/ha, and 10 kg of K/ha.
      Also, manure exports can be an important way to reduce NMB, especially for farms that operate with high animal densities (>2.4 AU/ha), as reported by others (
      • Powell J.M.
      • Jackson-Smith D.B.
      • Satter L.D.
      Phosphorus feeding and manure nutrient recycling on Wisconsin dairy farms.
      ;
      • Ketterings Q.M.
      • Knight J.
      • Ristow P.
      • Swanpoel G.
      • Czymmek K.
      Evaluation of dairy and cash grain farmers’ perceptions of the value of manure.
      ). New York CAFO regulations require manure export if a farm does not have adequate land base for allocation of manure in accordance with a nutrient management plan prepared using land grant university guidelines. Both dairy and crop farms recognize the value of manure (
      • Ketterings Q.M.
      • Knight J.
      • Ristow P.
      • Swanpoel G.
      • Czymmek K.
      Evaluation of dairy and cash grain farmers’ perceptions of the value of manure.
      ), and many NY dairy farms have elected to export manure where feasible to reduce long-term build up and the risk of future problems. In our NY database, 13 farms exported manure with an annual average export of 23 kg of N/ha, 3.3 kg of P/ha, and 12 kg of K/ha, with some farms exporting up to 127 kg of N/ha, 15 kg of P/ha, and 51 kg of K/ha, sufficient to balance nutrients below the 75% benchmark, despite the high animal density.

      Conclusions

      The wide range of NMB observed in our study highlights both variability and opportunity for improving balances over time. The NMB per hectare below which 75% of the dairies (averaged across all farm sizes) were operating amounted to 118 kg of N/ha for N1, 146 kg of N/ha for N2, 13 kg of P/ha, and 41 kg of K/ha. Seventy-five percent benchmarks for currently regulated farms (medium and large) were 164 kg of N/ha for N1, 201 kg of N/ha for N2, 16 kg of P/ha, and 65 kg of K/ha, reflecting a typically higher animal density for medium and large farms than for small farms. The most nutrient-efficient farms were operating with NMB per megagram of milk at or below 8.8 kg of N/Mg of milk for N1, 11.8 kg of N/Mg of milk for N2, 1.1 kg of P/Mg of milk, and 3.0 kg of K/Mg of milk. Farms operating with less than 2.4 AU/ha (~0.97 AU/acre) were typically able to meet the 75% benchmarks, whereas farms with >2.4 AU/ha needed to export manure or crops to lower their balance to meet the benchmark. This is also the maximum animal density for balancing cow P excretions and crop P removal without manure or crop exports in an example dairy farm assuming 70% homegrown forages and 0.4% P in the ration. Opportunities to reduce the NMB per hectare without negative effects on milk production exist by utilizing precision feeding, adjusting crop fertilization, increasing the percentage of farm produced nutrients, increasing feed use efficiency, and exporting crops and manure.

      Acknowledgements

      We thank all the New York State farmers, consultants, Soil and Water Conservation District and NRCS staff, and Cornell Cooperative Extension educators that participated in this study. Thanks also to Francoise Vermeylen from the Cornell University Statistical Consulting Unit for statistical advice. This work was supported by grants from the Northern New York Agricultural Development Program, Northeast Sustainable Agriculture Research and Extension, Federal-Formula Funds, and a USDA-NRCS Conservation Innovation Grant.

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