Adaptive management of stocking rates to reduce effects of drought on cow-calf production systems in semi-arid rangelands

We used simple ecological sustainability simulator (SESS) [Díaz-Solís, H., Kothmann, M.M., Hamilton, W.T., Grant, W.E., 2003. A simple ecological sustainability simulator (SESS) for stocking rate management on semi-arid grazinglands. Agric. Syst. 76, 655–680. <http://dx.doi.org/10.1016/j.agsy.2005.07.008>], modified to represent each of five management strategies (sets of decision rules) for adjusting stocking rates, to identify strategies that could reduce effects of drought on cow-calf production systems in semi-arid rangelands. We parameterized the model to represent a region of extensive cow-calf production in the northeastern portion of the Mexican state of Coahuila, and evaluated animal performance (animal body condition, cow mortality, and calf production) and range condition resulting from each strategy under random precipitation conditions typical of the region. To evaluate the validity of the randomly generated precipitation, we conducted the simulations under an historical (1950–1994) precipitation regime.The five management strategies included one with no adjustments to stocking rate (CONTROL, most common current practice), one with stocking rate adjustment rules based on changes in animal body condition (BCS), and three with different stocking rate adjustment rules based on various comparisons of recent-year precipitation with long-term mean precipitation during the growing season (March–November) (REPLA, PPT 1Y, PPT 2Y). Each strategy was evaluated at each of three initial base-level stock cow stocking rates (125, 250 and 500 AUY · 5000 ha⁻¹). Stocking rate adjustments consisted of partial or total de-stocking, with the re-establishment, or not, of the initial number of stock cows before the beginning of the subsequent breeding season.Results of 45-year simulations under both random and historical precipitation suggest CONTROL and PPT 2Y (based on comparison of current year and previous year precipitation with the long-term mean) strategies, combined with the high base-level stocking rate, are the worst and best, respectively. Under the historical precipitation regime, in the last period of time simulated (1980–1994) these two strategies resulted, respectively, in poor (0.5) versus good range condition (1.0), animal body condition scores of 2.6 versus 5.2, annual cow mortalities of 76 versus 5%, and calf production rates of 0.9 versus 10.6 kg ha⁻¹ year⁻¹ at weaning. The PPT 1Y strategy (based on comparison of current year precipitation with the long-term mean) produced results fairly similar to PPT 2Y, with BCS and REPLA strategies producing results intermediate between CONTROL and PPT 2Y. Our results suggest it is advantageous to adjust stocking rates based on precipitation during the current growing season since it improves cattle production without damaging range condition. Considering the practical feasibility of the strategies, we recommend the PPT 1Y strategy because it maintains range condition at moderate stocking rates, results in good animal performance and does not require total de-stocking of the ranch.The simple method we developed to stochastically generate monthly precipitation produces a time series of precipitation values that were representative of general historical precipitation patterns and provided realistic levels of uncertainty in simulated forage production to evaluate alternative management strategies.     

Dairy farm nutrient management model. 1. Model description and validation

Intensive dairy farming results in significant phosphorus (P) emission to the environment. Field data indicates that farm-gate P surplus is highly positive in Finland and strategies to mitigate the surplus are needed. The objectives of this study were to build a P cycle model for dairy farms (1) and to validate the model with independent field data (2). The dairy farm nutrient management model (“Lypsikki”) described in this paper includes three sub-models: (1) soil and crop, (2) dairy herd and (3) manure management. The model is based on empirical regression equations allowing estimations of crop and milk yields in response to increased fertilisation and nutrient supply, respectively. In addition, the model includes a dynamic simulation model of the dairy herd structure and calculation of the farm-gate nutrient surplus. The model was validated with independent annual (average for 1-4 years) farm-gate P surplus data from 21 dairy farms. Model simulations were conducted using two levels of soil productivity, mean (M) and low (L). The model validation indicated a strong relationships between model-predicted and observed farm-gate P surplus: (M: R² = 0.77 and L: R² = 0.80). The line bias between the model-predicted and observed data was negligible and insignificant (P > 0.6) suggesting a robustness of the model. The mean biases were relatively high and significant (M: 4.7 and L: 1.8 kg/ha, P < 0.001), but evidently related to overestimation of crop yields that has to be taken into account when using the model on a single farm. The prediction error of the model (observed minus predicted P surplus) was significantly correlated to the difference between simulated and observed P import in feeds (M: R² = 0.55 and L: R² = 0.51). This suggests either that all the dairy farms did not fully exploit the possibilities in the crop production or that all the model assumptions are not correct. The effects of purchased feed and fertiliser P and exported milk P (per cow or cropping area) on farm-gate P surplus were of the same magnitude in both observed and simulated data. This implies that the model developed can be used as a management decision tool to find strategies to mitigate P surplus on dairy farms.     

Heterogeneity of crop productivity and resource use efficiency within smallholder Kenyan farms: Soil fertility gradients or management intensity gradients?

The decrease in crop yields at increasing distances from the homesteads within smallholder farms of Sub-Saharan Africa (SSA) is normally ascribed to the existence of within-farm soil fertility gradients. Field observations also suggest that a large part of such variability is concomitantly caused by poor agronomy. To understand the interaction between soil fertility (S factors) and management decisions (M factors) affecting crop variability, we combined field research conducted in western Kenya (Vihiga, Kakamega and Teso districts; rainfall: 1600, 1800 and 1200 mm, respectively) with explorations using the simple dynamic crop/soil model for dynamic simulation of nutrient balances, previously tested for the region. Field measurements indicated within-farm differences in average maize grain yields of 48% (2.7 vs. 1.4 t ha⁻¹) in Vihiga and of 60% (1.5 vs. 0.6 t ha⁻¹) in Teso, between fields that were close and far from the homestead, respectively. Extreme values ranged widely, e.g. between 4.9 and 0.3 t ha⁻¹ for all the farms surveyed in Vihiga, where the average farm size was 0.6 ha. Maize grain yields tended to increase with increasing contents of soil C, total N, extractable P and exchangeable bases. However, the negative relationship between S factors and distance from the homestead was not as strong as expected, and yield variability was better explained by multiple regression models considering M factors such as planting date, plant density, resource use and weed infestation (40–60% across sites). Then, we analysed the variation in resource (cash, labour, N) use efficiency within farms of different resource endowments with the aid of the simulation model. N balances at plot scale varied from ca. +20 to −18 kg ha⁻¹, from −9 to −20 kg ha⁻¹ and from −16 to −18 kg ha⁻¹ for the different fields of the high, medium and low resource endowment case-study farms, respectively. Labour productivities ranged between ca. 10 and 38 kg grain man-day⁻¹ across field and farm types. The results indicate the need of considering within farm heterogeneity when designing soil fertility management interventions. Resource use efficiency was strongly affected by soil quality. As farmers invest more effort and resources in the more productive and less risky fields, the interaction between S and M factors leads to farmer-driven resource use efficiency gradients within smallholder farms.     

Life cycle assessment of greenhouse gas emissions from beef production in western Canada: A case study

A life cycle assessment (LCA) was conducted to estimate whole-farm greenhouse gas (GHG) emissions from beef production in western Canada. The aim was to determine the relative contributions of the cow-calf and feedlot components to these emissions, and to examine the proportion of whole-farm emissions attributable to enteric methane (CH₄). The simulated farm consisted of a beef production operation comprised of 120 cows, four bulls, and their progeny, with the progeny fattened in a feedlot. The farm also included cropland and native prairie pasture for grazing to supply the feed for the animals. The LCA was conducted over 8 years to fully account for the lifetime GHG emissions from the cows, bulls and progeny, as well as the beef marketed from cull cows, cull bulls, and progeny raised for market. The emissions were estimated using Holos, a whole-farm model developed by Agriculture and Agri-Food Canada. Holos is an empirical model, with a yearly time-step, based on the Intergovernmental Panel on Climate Change methodology, modified for Canadian conditions and farm scale. The model considers all significant CH₄, N₂O, and CO₂ emissions and removals on the farm, as well as emissions from manufacture of inputs (fertilizer, herbicides) and off-farm emissions of N₂O derived from nitrogen applied on the farm. The LCA estimated the GHG intensity of beef production in this system at 22 kg CO₂ equivalent (kg carcass)⁻¹. Enteric CH₄ was the largest contributing source of GHG accounting for 63% of total emissions. Nitrous oxide from soil and manure accounted for a further 27% of the total emissions, while CH₄ emissions from manure and CO₂ energy emissions were minor contributors. Within the beef production cycle, the cow-calf system accounted for about 80% of total GHG emissions and the feedlot system for only 20%. About 84% of enteric CH₄ was from the cow-calf herd, mostly from mature cows. It follows that mitigation practices to reduce GHG emissions from beef production should focus on reducing enteric CH₄ production from mature beef cows. However, mitigation approaches must also recognize that the cow-calf production system also has many ancillary environmental benefits, allowing use of grazing and forage lands that can preserve soil carbon reserves and provide other ecosystems services.     

The diversity of rural livelihoods and their influence on soil fertility in agricultural systems of East Africa - A typology of smallholder farms

Technological interventions to address the problem of poor productivity of smallholder agricultural systems must be designed to target socially diverse and spatially heterogeneous farms and farming systems. This paper proposes a categorisation of household diversity based on a functional typology of livelihood strategies, and analyses the influence of such diversity on current soil fertility status and spatial variability on a sample of 250 randomly selected farms from six districts of Kenya and Uganda. In spite of the agro-ecological and socio-economic diversity observed across the region (e.g. 4 months year⁻¹ of food self-sufficiency in Vihiga, Kenya vs. 10 in Tororo, Uganda) consistent patterns of variability were also observed. For example, all the households with less than 3 months year⁻¹ of food self-sufficiency had a land:labour ratio (LLR) < 1, and all those with LLR > 1 produced enough food to cover their diet for at least 5 months. Households with LLR < 1 were also those who generated more than 50% of their total income outside the farm. Dependence on off/non-farm income was one of the main factors associated with household diversity. Based on indicators of resource endowment and income strategies and using principal component analysis, farmers’ rankings and cluster analysis the 250 households surveyed were grouped into five farm types: (1) Farms that rely mainly on permanent off-farm employment (from 10 to 28% of the farmers interviewed, according to site); (2) larger, wealthier farms growing cash crops (8-20%); (3) medium resource endowment, food self-sufficient farms (20-38%); (4) medium to low resource endowment relying partly on non-farm activities (18-30%); and (5) poor households with family members employed locally as agricultural labourers by wealthier farmers (13-25%). Due to differential soil management over long periods of time, and to ample diversity in resource endowments (land, livestock, labour) and access to cash, the five farm types exhibited different soil carbon and nutrient stocks (e.g. Type 2 farms had average C, N, P and K stocks that were 2-3 times larger than for Types 4 or 5). In general, soil spatial variability was larger in farms (and sites) with poorer soils and smaller in farms owning livestock. The five farm types identified may be seen as domains to target technological innovations and/or development efforts.     

Farm types for beef production and their economic success in a mountainous province of northern Vietnam

The objective of this study was to compare the management and economic success of beef production by three types of farm in northwestern Vietnam. The potential of household farms to supply beef for the market and their competition with large farms were examined.The fieldwork was done in 2007 on 73 farms consisting of 58 small mixed farms (small farms), 10 medium mixed farms (medium farms) and 5 specialised large-scale beef farms (large farms) in Son La province. The three types of farm differed in ethnicity (Thai, H’mong, and Kinh), remoteness (lowland, highland), production objectives (subsistence, market output), degree of specialization (mixed farm, specialised beef farm) and integration of production (single farmers, cooperative). Data on biological productivity, inputs and outputs, and the social contribution of cattle production were collected by household and key person interviews, participatory rural appraisal tools and cattle body measurements. Economic values were derived by assessment of market or replacement costs. Quantitative data analysis was done with linear models (PROC GLM) in the SAS software (version 9.1).Lowland small farms had higher costs for cattle production than the highland farms (0.8 Mill. VND head⁻¹ year⁻¹ compared with 0.02 Mill. VND head⁻¹ year⁻¹, respectively). The large farms had high production costs, with an average of 2.5-3.6 Mill. VND head⁻¹ year⁻¹. Cattle brought high benefits of non-cash values to the household farms. The total revenue from cattle was in the range 4.5-11.5 Mill. VND head⁻¹ year⁻¹, which depended on the use of non-market functions of cattle on the household farm. The value of net benefit/kg live weight (LW) of lowland small farms with an average of 39,000 VND/kg LW was significantly higher than that of the medium and small farms in the highlands (26,000 VND/kg LW). However, the small farms kept fewer cattle than the medium farms (average of 2-4 cattle/farm compared with 9 cattle/farm, respectively) because of forage and labour shortages and have no option to further develop cattle production. Keeping larger numbers of cattle based on available natural pasture brought high benefit from stock value as farm liquidity to only the medium farms. This was the most promising type of farm for future development of beef production, given its actual success and the availability of underutilised resources. Large-scale farms suffered high economic losses of 0.3-1.4 Mill. VND cattle⁻¹ year⁻¹, due to the lack of professional management, high feed costs and low animal performance, and showed no potential for developing cattle production.     

Financial implications of recent declines in reproduction and survival of Holstein-Friesian cows in spring-calving Irish dairy herds

Milk production and reproductive performance were monitored in 14 spring calving dairy herds in the south of Ireland between 1990 and 2003. In these herds, the average pedigree index for milk yield increased by 25 kg per year from 1990 to 2001, while the average proportion of Holstein-Friesian genes in the cows increased from 8% in 1990 to 63% in 2001. Over this period, milk production per cow increased by 54 kg/year, while replacement rate increased from 16% in 1990 to 27% in 2003. To evaluate the farm-level financial implications of associated changes in calving pattern, milk production and replacement rate, data from the 14 spring-calving herds were included in the Moorepark Dairy System Model for each of the 14 years. Two milk production scenarios were investigated, which included EU milk quota applied at farm level (S1) and no milk quota (S2). The influence of variation in milk price, cull cow value, replacement heifer cost and replacement rate were modelled using stochastic budgeting. In S1 there was a significant linear increase (P < 0.05) in margin per cow (€10.8), margin per kg of milk produced (0.13 cent) and net farm profit (€546) over the 14-year period. Similarly in S2 there was a significant linear increase in margin/cow (€11.3), margin/kg (0.14 cent) and farm profit (€1089) over the 14-year period. However, the analysis showed that if reproductive performance, calving spread and replacement rate could have been maintained at 1990 levels for each of the 14 years then the increase per cow, per kg of milk and farm profit per year would have been €22.1, 0.31 cent and €1341 for S1, and €22.8, 0.32 cent and €2183 for S2, respectively. Stochastic analysis showed that farm profit was most sensitive to changes in milk price, followed by replacement rate.     

Pasture and forage crop systems for non-irrigated dairy farms in southern Australia: 3. Estimated economic value of additional home-grown feed

Dairy systems in southern Australia rely on grazed feed from pasture to supply between 50% and 70% of total herd feed requirements on an annual basis. However, the dominant pasture type in the region, which is based on perennial ryegrass (Lolium perenne), commonly results in feed deficits in summer which must be filled with supplements purchased off-farm, and feed surpluses in spring which must be conserved. Both of these strictures impose costs on farm businesses. It is likely, therefore, that additional grazeable feed available to dairy herds in southern Australia may have different economic value when interactions between season, stocking rate, calving date, and locality are taken into account. The analysis reported in this paper aimed to estimate, using the farm systems simulation model UDDER, the effect of these interactions on the efficiency with which extra feed can be converted to extra milk production, and therefore the possible gross economic value of the additional feed.‘Base’ farm simulations for ‘average’ and ‘top 10%’ farms (ranked according to farm profitability) in two localities (Terang: average annual rainfall 796 mm, 8 month growing season; and Ellinbank: average annual rainfall 1085 mm, 9-10 month growing season) were created to mimic the physical production and profitability of these farms as seen in regional farm benchmark datasets. These simulations were then altered to add the equivalent of 10% of the total annual herbage accumulation used in the Base simulation either on a pro-rata basis all year round, or in autumn only, in winter only, in spring only, or in summer only. The additional feed amounted to 620 and 780 kg DM/ha for Terang average and top 10% farms respectively, and 735 and 905 kg DM/ha for Ellinbank average and top 10% farms respectively. The management policies used in the Base simulations were then adjusted to harvest as much of the extra feed as possible, either by direct grazing or through silage conservation, while keeping the key system state indicators of cow condition score and average farm pasture cover within the limits known to result in long-term sustainable production.The efficiency with which extra feed was utilised was greatest in summer in all scenarios (80-100% of the extra feed supplied was harvested, all by direct grazing). This translated into consistently high gross economic returns of between $0.26 and $0.34 per kg DM of extra feed added to the model. Utilisation efficiency was lower in all other seasons and/or required marked increases in silage conservation, both of which resulted in lower gross economic returns per kg DM of additional feed. The impact of interactions between locality, season, stocking rate (higher in top 10% farm simulations than average farm simulations) and calving date (earlier at Terang than at Ellinbank) were clearly captured in the model. These interactions have very large effects on the profitability of growing extra feed at different times of the year. Agronomic research for the southern Australia dairy industry should focus on low-cost ways for supplying additional grazeable feed in summer, since current forage species options for this time of year are limited.     

Replacement policy in dairy herds on farms where heifers compete with cows for grassland: Part 2-Experimentation

Replacement policy is not easy to determine on dairy farms where heifers compete with cows for grassland. Using a computer simulation model of this farm situation, two factorial experiments were conducted to evaluate quantitatively the effect of different replacement rates on profitability and herd improvement.The variables changed in herds of average health and very good health were replacement rate (0·14, 0·22, 0·30), age at first calving (36, 24 months), calving index (13, 12 months) and AI sire merit (standard, premium). Initially, the experimental herd had average health, a replacement rate of 0·22, a calving index of 13 months, calved its heifers at 36 months and had been using standard bulls for many years.After 15 years, the increase in the level of a sinking fund when the age at first calving was reduced was between three and five times greater than when premium bulls were used, calving index was reduced or herd health was improved. There was a major interaction between replacement rate and age at first calving.Yield per cow was significantly reduced (P≤0·001) by reducing the age at first calving and significantly increased when premium bulls (P≤0·001) were used or when herd health was improved (P≤0·05).Some treatments were not tested as expected due to the restraining effect of a 13-month calving interval on the availability of cows for breeding pure in a seasonally calving herd.     

Comparative economics of Holstein/Gir F1 dairy female production and conventional beef cattle suckler herds – A simulation study

Three cow–calf production systems were compared using simulation: N (straightbred Nelore), AN (Nelore cows producing Angus by Nelore calves) and HG (Gir cows producing Holstein by Gir calves). All three systems produced their own straightbred replacement females. Male calves were sold at weaning and female calves in excess of those required to keep the herd size constant were sold at one year of age. In the base situation, F₁ HG females were priced at twice as much as the price per kg of the beef male calves, according to present market values. Typical 1000 ha beef cattle farms were simulated for each system, based on Brachiaria brizantha pastures managed according to recommended practices. Herd dynamics were controlled by reproduction and survival. Literature figures on monthly pasture nutrient production, live weights and milk yield were used to estimate nutrient requirements to match stocking rate to nutrient availability in each system. For calving rate set to 0.8 in all three systems, the total numbers of cows for the N, AN and HG systems were, respectively, 803, 795 and 885 and the total live weight sold annually was 129,070, 133,120 and 127,680 kg. The annual economic return on investment was 5.21%, 5.81% and 10.84%, respectively, for the N, AN and HG systems. Reducing the relative price of the HG heifers diminished the economic superiority of this system over N and AN. The difference was zero when the price of HG heifers was reduced to approximately 1.2 times the beef calf price. This also happened when the calving rate of the Gir cows was set to 0.6 keeping N cows at 0.8 or higher.     

Replacement policy in dairy herds on farms where heifers compete with cows for grassland—Part 3: A revised hypothesis

A preliminary investigation of replacement policy on dairy farms where heifers compete with cows for grassland suggested that profitability falls as replacement rate increases, because the number of cows able to be milked and the maturity of the herd override the gains in milk yield per cow due to genetic improvement by culling for insufficient yield and by the greater use of a sire of high genetic merit.This hypothesis was inadequate to cover all the situations investigated in two factorial experiments involving changes in herd health status, replacement rate, age at first calving, calving index and the merit of AI sires used in the herd from an initial state for these variables of 0·22, 36 months, 13 months and Standard, respectively.Overall, these was a clear indication that the major influence on profitability was the number of cows in the herd. An increase in replacement rate was not always associated with a fall in profitability, nor was an improvement in yield per cow always associated with a rise in profitability relative to the performance of a control herd after 15 simulated years.The manipulation of replacement rate and age at first calving to achieve an increase in the number of cows in the herd was not necessarily incompatible with a desire for herd improvement either phenotypically (yield per cow) or genetically (herd and heifer genotype), particularly if premium bulls were used.     

Application of a simple ecological sustainability simulator (SESS) as a management tool in the semi-arid rangelands of northeastern Mexico

We use a simple ecological sustainability simulator (SESS) [Diaz-Solis, H., Kothmann, M.M., Hamilton, W.T., Grant, W.E., 2003. A simple ecological sustainability simulator (SESS) for stocking rate management on semi-arid grazinglands, 76, 655] for rangelands with mean annual precipitation of 500 mm to evaluate tendencies in range productivity and cattle production under four management options: (1) supplemental feeding, (2) short-term reduction of stocking rate, (3) early weaning, and (4) adjustment of breeding seasons. We have made five modifications to SESS for the present paper. (1) Cattle mortality now occurs each month as a function of body condition. (2) Cows that are not pregnant 2 months after the end of the breeding season are sold. (3) Forage intake is calculated separately for each cohort of cows. (4) Cows that have been sold or have died are replaced just before the beginning of each breeding season (except for the short-term reduction of stocking rate strategy). (5) The calculation of stocking rate now includes cows, bulls, nursing calves, weaned heifers less than 20 months of age, and pre-reproductive heifers aged 20 months or older.Simulation results suggest the four management options might be ranked from best to worst, in terms of increasing cattle production while maintaining range productivity, as: (1) short-term reduction of stocking rate, (2) adjustment of breeding seasons, (3) early weaning, and (4) supplementation. Short-term reduction of high stocking rates reduces the deterioration of range productivity because of the reduction in the number of stock. Adjustment of breeding seasons such that periods of highest energy requirements of cows and calves coincide with periods of highest forage production increases percentage pregnancy. Early weaning of calves improves the body condition of cows and increases annual production of weaned calves, but does not reduce the stocking rate and thus does not improve range productivity. Supplemental feeding, and other management practices that artificially sustain herbivores, break the negative feedback that promotes good range productivity and maintains long-term system stability. In general, strategies to increase cattle production in semi-arid rangelands should be based on the improvement of natural forage production.     

Identifying and controlling critical sources of farm phosphorus imbalances for Vermont dairy farms

Lake Champlain, located between Vermont, New York, and Quebec exhibits eutrophication mainly due to continuing phosphorus (P) losses from upstream nonpoint source areas. Several state and local agencies have initiated efforts aimed at assessing and identifying critical sources areas for P loss. To augment these efforts, accounting of farm P inputs (in purchased animal feed and fertilizers) and P outputs (in milk, meat, or off-farm sales of harvested crops or other products) is needed as a means of determining potential P build-up in farm soils. When farm P inputs exceed P outputs, P surplus occurs on the farm. This leads to potential soil-P accumulations and risk of P loss in runoff, negatively impacting the quality of receiving water bodies. In this study, a combination of farm record data and a model-based approach, using the Integrated Farming System Model (IFSM), was used to estimate farm P inputs and outputs, identify root causes of farm P imbalances, and explore viable P balancing strategies. Three Vermont dairy farms with varying farm systems (grass-based organic farm, fully confined farm, and a mixed system farm with high-producing confined dairy cows and grazing heifers) were studied. These farms were found to have P surpluses ranging from 5.5 kg/ha to 18.7 kg/ha on annual basis. This study also identified critical causes of P imbalances for each farm and suggested farm specific alternative strategies needed to address the P imbalances. By balancing farm P inputs and outputs, potential accumulation of soil-P can be prevented. As a result, maximum benefits can be obtained from land treatment measures implemented to control off-field P loss without the additional concern of continuing P build-up that could reduce their effectiveness.     

Strategies to decrease water drainage and nitrate emission from soilless cultures of greenhouse tomato

In the spring-summer season of 2005 and 2006, we explored the influence of three fertigation strategies (A-C) on the water and nitrogen use efficiency of semi-closed rockwool culture of greenhouse tomato conducted using saline water (NaCl concentration of 9.5 mol m⁻³). The strategies under comparison were the following: (A) crop water uptake was compensated by refilling the mixing tank with nutrient solution at full strength (with the concentrations of macronutrients equal or close to the corresponding mean uptake concentrations as determined in previous studies) and the recirculating nutrient solution was flushed out whenever its electrical conductivity (EC) surpassed 4.5 dS m⁻¹ due to the accumulation of NaCl; (B) the refill nutrient solution had a variable EC in order to maintain a target value of 3.0 dS m⁻¹; due to the progressive accumulation of NaCl, the EC and macronutrient concentrations of the refill nutrient solution tended to decrease with time, thus resulting in a progressive nutrient depletion in the recycling water till N-NO₃⁻ content dropped below 1.0 mol m⁻³, when the nutrient solution was replaced; (C) likewise Strategy A, but when EC reached 4.5 dS m⁻¹, crop water uptake was compensated with fresh water only in order to reduce N-NO₃⁻ concentration below 1.0 mol m⁻³ before discharge. In 2005 an open (free-drain) system (Strategy D), where the plants were irrigated with full-strength nutrient solution without drainage water recycling, was also tested in order to verify the possible influence of NaCl accumulation and/or nutrient depletion in the root zone on crop performance. In the semi-closed systems conducted following strategies A, B or C, the nutrient solution was replaced, respectively, 10, 14 and 7 times in 2005, and in 19, 24 and 14 times in 2006, when the cultivation lasted 167 days instead of 84 days in 2005. In both years, there were no important differences in fruit yield and quality among the strategies under investigation. Strategy C produced the best results in terms of water use and drainage, while Strategy B was the most efficient procedure with regard to nitrogen use. In contrast to strategies A and D, the application of strategies B and C minimized nitrogen emissions and also resulted in N-NO₃⁻ concentrations in the effluents that were invariably lower than the limit (approximately 1.42 mol m⁻³) imposed to the N-NO₃⁻ concentration of wastewater discharged into surface water by the current legislation associated to the implementation of European Nitrate Directive in Italy.     

The profitability and risk of dairy cow wintering strategies in the Southland region of New Zealand

A survey amongst stakeholders in 2007 identified wintering systems with less environmental impact and a reliable supply of high quality feed, which are cost effective and simple to implement, as one of the top three issues requiring research and demonstration in the Southland region of New Zealand. This study used a modelling approach to examine the cost effectiveness, exposure to climate-induced risk and major economic drivers of four selected wintering strategies, i.e. (1) grazing a forage brassica crop on support land (Brassica system), (2) grazing pasture on support land (All pasture system), (3) cows fed grass silage, made on the support land, on a loafing pad where effluent is captured (Standoff system), and (4) cows fed grass silage, made on the support land, in a housed facility where effluent is captured (Housed system). The model was driven by virtual climate data generated by the National Institute of Water and Atmospheric Research and economic input data from the DairyNZ Economics Group for the 08/09 season with a milk price of NZ$4.55¹/kg milksolids (fat + protein). The Housed system had the highest average (± STDEV) operating profit (profit after depreciation but before interest charges) over 35 independently simulated climate years (NZ$743 ± 122/ha), followed by All pasture (NZ$681 ± 197/ha), Standoff (NZ$613 ± 135/ha) and Brassica (NZ$599 ± 212/ha). This ranking was sensitive to the assumptions and treatment of capital costs. The Housed system was the least exposed to climate-induced risk with a coefficient of variation of operating profit of 16% compared to 35% of the Brassica system. The four systems demonstrated different financial strengths and weaknesses that largely balanced out in the end. The Brassica system is a high risk system from an environmental perspective and the All pasture system an unlikely alternative because of scarcity of suitable land. Both the Housed and Standoff systems appear to be cost effective alternatives that allow high control over cow feeding, body condition and comfort over winter. Furthermore, both systems have the potential to provide high control over the storage and release of animal effluent onto land, thus saving fertiliser costs and reducing environmental footprint.     

Raising surface water levels in peat areas with dairy farming: Upscaling hydrological, agronomical and economic effects from farm-scale to local scale

Raising surface water levels in peat areas is a measure to reduce soil subsidence, to prevent decay of wooden foundations and to stimulate wet nature restoration and reduce greenhouse gas emissions. However, in these areas dairy farms are present and farming at wetter soils is difficult due to lower bearing capacity of the soil for cattle and machines. Water boards are responsible for the water management of peat areas and thus have to evaluate the effects of water management strategies for the different land use functions. Therefore the hydrological, agronomical and economic effects of different surface water levels are calculated for dairy farms. The ‘Waterpas’ model is used to simulate hydrological effects, dairy farm management and economic results for different meteorological years. The raised surface water level causes a decrease in gross grass yield and a reduction in grass quality. This leads to higher costs and less farmers’ income relative to a reference situation with a freeboard of 60 cm. Raising the surface water increases the average costs for farmers with €89 ha⁻¹ year⁻¹ for a freeboard of 50 cm, €170 ha⁻¹ year⁻¹ for a freeboard of 40 cm and €239 ha⁻¹ year⁻¹ for a freeboard of 30 cm.However, water boards are not only interested in the effects for individual farms, but also for an entire region. A new spatial method was developed for upscaling from farm to polder level. For grassland fields in a typical Dutch peat area classes can be distinguished using GIS data on soil type, soil surface elevation, surface water levels, locations of farms and farm characteristics. The classification is based on 4 classes of freeboards of the grassland fields and 7 typical distributions of grassland fields within a dairy farm. The farm economics were simulated for these typical classes. An increase in costs was simulated for the whole polder Zegveld (1400 ha grassland) of €119,000 year⁻¹ at 10 cm surface water level rise; €133,000 year⁻¹ at 20 cm surface water level rise and €185,000 year⁻¹ at 30 cm surface water level rise.For an integral environmental evaluation of changing hydrological conditions it is advised to incorporate effects on nutrient emission to groundwater and surface water and emission of ammonia and greenhouse gases to the atmosphere.     

Differential evolution algorithm for solving multi-objective crop planning model

This study presents four strategies of a novel evolutionary algorithm, multi-objective differential evolution algorithm (MDEA). The four strategies namely, MDEA1, MDEA2, MDEA3 and MDEA4 are adapted to solve the multi-objective crop planning model with multiple constraints in a farmland in the Vaalharts irrigation scheme (VIS) in South Africa. The three objectives of the model are to minimize the total irrigation water (m²) and to maximize both the total net income in South African Rand (ZAR) from farming and the total agricultural output in tons. The total area of the farm is 771,000 m² and supplied with 704,694 m² of irrigation water annually. Numerical results produce non-dominated solutions which converge to Pareto optimal fronts. MDEA1 and MDEA2 strategies with binomial crossover method are better for solving the crop planning problem presented than MDEA3 and MDEA4 strategies with exponential crossover method. MDEA1 found a solution with the highest total net income of ZAR 1,304,600 with the corresponding total agricultural output, total irrigation water and total planting areas of 316.26 tons, 702,000 m³ and 725,000 m², respectively. The planting areas for the crops in the solution are 73,463 m² for maize, 551,660 m² for groundnut, 50,000 m² for Lucerne and 50,000 m² for Peacan nut. It can be concluded that MDEA is a good algorithm for solving crop planning problem especially in water deficient areas like South Africa.     

Energy consumption, greenhouse gas emissions and economic performance assessments in French Charolais suckler cattle farms: Model-based analysis and forecasts

The environmental and economic performance of five Charolais beef production systems (three specialized beef producer test cases in grassland areas and two mixed crop-livestock test cases with a more intensive production system) were assessed by coupling an economic optimization model (“Opt’INRA”) with a model assessing non-renewable energy (NRE) consumption and greenhouse gas emissions (“PLANETE”). The test cases studied covered a relatively diverse range of raised and sold animals: calf-to-weanling or calf-to-beef systems (animals sold: from 10-month-old weaners to 36-month-old beef steers). In 2006, NRE consumption ranged from 26,440 to 31,863 MJ/ton of live weight produced over 1 year. Fuels and lubricants were the main factors of NRE consumption, followed by fertilizers and farm equipment. Livestock was the main driver of global warming potential. GHG emissions, at 14.3-18.3 tCO₂eq/t LW, were mainly determined by the proportion of cows in the total herd livestock units, according to the farming system deployed, i.e. calf-to-weanling vs. calf-to-beef. Against a background of rising energy costs, farms running mixed crop-livestock systems enjoy greater flexibility to adjust their farming systems than grassland-based farms, enabling them to minimize the drop in income over the timeframe to 2012 (−3%). In this same setting, specialist beef producers face a 15-25% drop in income. In all the scenarios run, system adjustments designed to minimize the drop in income have only a very limited impact on NRE consumption and GHG emissions.     

Monitoring forage production for farmers’ decision making

Objective management of grazing livestock production systems needs monitoring of forage production at the managerial unit level. Our objectives were to develop a system that routinely estimates forage above-ground net primary production (ANPP) at the spatial and temporal resolution required by farmers in the Pampas of Argentina, and to facilitate adoption of the system by end users as a managerial support tool. Our approach was based on the radiation use efficiency (RUE) logic, which proposes that ANPP is determined by the amount of photosynthetically active radiation absorbed by the canopy (APAR), and the efficiency with which that energy is transformed in above-ground dry matter (radiation use efficiency, RUE). APAR is the product of incoming photosynthetically active radiation (PAR) and the fraction absorbed by the canopy (fPAR). We estimated fPAR as a non-linear function of MODIS normalized difference vegetation index (NDVI). RUE was empirically estimated for the two principal forage resources of the region, yielding the following relations: ANPP = 0.6 × APAR + 12, (R² = 0.86; p < 0.001; n = 18) for the upland sown pastures, and ANPP = 0.27 × APAR + 26, (R² = 0.74; p < 0.001; n = 18) for the lowland naturalized pastures, with ANPP in g/m²/60 days and APAR in MJ/m²/60 days. The models were able to predict independent ANPP values with acceptable accuracy. Computational procedures were automated and run in a Relational Data Base Manager System that stored and managed all the information. The system is currently monitoring 212,794 ha in 83 farms and provides monthly ANPP values for the previous month and a history of the last 6 years. The data so generated show ANPP differences between the two major forage resources, considerable variability of a given month’s ANPP among years and paddocks, and contrasting among-farm differences in the efficiency of conversion of ANPP and forage supplements into beef production. The system was well accepted by end users who utilize it mainly for making near real time decisions according to last month ANPP, and explaining results of previous production cycles by incorporating ANPP as an explicative variable. However, there were differences among farmers in the degree of utilization, apparently related to the advisor’s attitude toward this new technology. Our results indicate that (1) forage production of large extensions can be monthly monitored at the paddock level by a small laboratory with capabilities in geographic information systems, and (2) advisors and farmers apply this information to their managerial decisions.