Summary 1.Reintroductions provide a good opportunity to study density-dependent population growth, as populations can be studied at a range of densities and the change in density is not confounded with environmental conditions. An understanding of density dependence is also necessary to predict dynamics of reintroduced populations under different management regimens, and assess the extent to which they can be harvested for further reintroductions. 2. We monitored a North Island saddleback ( Philesturnus rufusater ) population for 6 years after reintroduction to Mokoia, a 135 ha island in New Zealand that was made suitable for saddlebacks by eradicating introduced Norway rats ( Rattus norvegicus ). We modelled adult and juvenile survival using Program  , and modelled numbers of young fledged per pair using Proc Mixed in SAS with individual female as a random factor. 3. Juvenile survival clearly declined as the population increased, and the decline was closely correlated with the number of breeding pairs. Reproduction also showed a clear decline that was explained by two factors: a difference in quality between territories occupied immediately after reintroduction and those occupied later, and an overall decline as the number of pairs increased. Reproduction was also strongly affected by age, and this needed to be accounted for when modelling density dependence. 4. A stochastic simulation model incorporating these dynamics closely predicted the observed population growth. The equilibrium population size was insensitive to density dependence in reproduction, but highly sensitive to density dependence in juvenile survival. 5. The model is being used to plan management strategies for potential reintroductions of saddlebacks to mainland areas with predator control. The species is currently confined to predator-free islands and one fenced mainland sanctuary.

Variable rate technology enables management of individual soil types within fields. However, correct classification of soil types for mid‐Atlantic coastal plain soils are currently impractically expensive using an Order I Soil Survey, yet variable rate fertilizer application based on soil type can be highly effective. The objectives of this study were to determine if apparent electromagnetic conductivity (ECa) alone or combined with previous year crop yields using global positioning system technology can provide a useful alternative to detailed soil mapping. The site contained alluvial soils ranging from Bojac 1 and 2 (coarse‐loamy, mixed, thermic, Hapludults) to Wickham 3 and 4 (fine‐loamy, mixed, thermic, Ultic Hapludalfs). The two fields totaled approximately 24 ha. A statistical nonparametric classification method, called recursive binary classification trees, was used to determine how well soil types could be classified. Electromagnetic conductivity readings and crop yields were positively correlated. Broad patterns in the relationship between soil types and ECa readings and crop yields existed for all crop combinations considered. Lower ECa readings and crop yields corresponded to the Bojac soils, while higher ECa readings and crop yields were categorized as Wickham soils. Electromagnetic induction alone correctly classified the soils into broad categories of Bojac or Wickham with over 85% accuracy. When ECa was combined with crop yield data, correct classification rose to over 90%. More precise classification into Bojac 1, Bojac 2, and Wickham soils yielded slightly lower correct classifications ranging from 62.6 to 81.2% for ECa alone, and 80.3 to 91.5% when combined with various crop yields.

Double‐crop soybean (Glycine max L.) often yields less than full‐season soybean, due in part to decreased leaf area development. Information is lacking on the effect of cropping system and plant‐available water holding capacity (PAWHC) as affected by soil type on LAI in field‐scale environments. The objectives of this research were to (i) determine the effect of three cropping systems and three soil types that vary in PAWHC on double‐crop soybean leaf area and yield; and (ii) validate the LAI–yield relationship in a field‐scale experiment. Soybean LAI and yield were measured at random geographically located positions during the 1999 to 2001 cropping seasons. During years of early season or little drought stress, LAI and yield were 1.1 to 1.2 units and 480 to 558 kg ha−1 less on the lowest PAWHC soil compared with the soil with the highest PAWHC. Soybean LAI and yield were reduced 0.4 units and 301 kg ha−1 when soybean experience early season drought stress and was not rotated. Under conditions of late‐season drought stress, LAI and yield of rotated soybean were 1.6 units and 1350 kg ha−1 less on the lowest PAWHC soil. When soybean was not rotated and under conditions of late‐season drought stress, LAI and yield were reduced by 2.9 units and 1770 kg ha−1 for the soil with the lowest PAWHC, and 1.6 units and 760 kg ha−1 for the soil with intermediate PAWHC. Soybean LAI was linearly related to yield only on the lowest PAWHC soil where LAI was low.

Water is the main limiting factor to rainfed corn (Zea mays L.) yield in the mid‐Atlantic coastal plain. This study was conducted to determine the water balance, yields, and efficiency of water use (EWU) of no‐till rainfed corn grown on three soils of varying water‐holding capacity, a Wickham sandy loam (fine‐loamy, mixed, thermic, Typic Hapludults) and two Bojac soils, a sandy loam and a loamy sand (coarse‐loamy, mixed, thermic, Typic Hapludults). Soil water balance was determined from climate data and weekly measurements of soil moisture from time domain reflectometry (TDR) probes. Water balance components of water stress, crop coefficients, and evapotranspiration were determined for vegetative, tasseling, and grain‐fill growth stages in 1998 and 1999. Yields were determined by overlaying georeferenced yield maps, order‐1 soil survey maps, and locations of TDR probes. The EWU concept was defined as yield divided by [precipitation + (initial soil water content − final water content)], clarifying it from water use efficiency measurements that are calculated similarly but do not include drainage. Yields in 1998 (4833–12200 kg ha−1) were higher than 1999 yields (2245–8240 kg ha−1) due to higher growing season precipitation in 1998 (400 mm) than 1999 (220 mm). Drainage was determined in 1998, ranging from 65 to 105 mm. Minimizing drainage losses has potential for increasing the EWU and yields. This study establishes baseline water balance data for the mid‐Atlantic coastal plain that can be used to parameterize computer models for investigating the effect of management practices on EWU and yields.

Better management of water quality in streams, rivers and lakes requires precise and accurate estimates of different contaminant loads. We assessed four sampling frequencies (2 days, weekly, fortnightly and monthly) and five load calculation methods (global mean (GM), rating curve (RC), ratio estimator (RE), flow-stratified (FS) and flow-weighted (FW)) to quantify loads of nitrate-nitrogen (NO-N), soluble inorganic nitrogen (SIN), total nitrogen (TN), dissolved reactive phosphorus (DRP), total phosphorus (TP) and total suspended solids (TSS), in the Manawatu River, New Zealand. The estimated annual river loads were compared to the reference 'true' loads, calculated using daily measurements of flow and water quality from May 2010 to April 2011, to quantify bias (i.e. accuracy) and root mean square error 'RMSE' (i.e. accuracy and precision). The GM method resulted into relatively higher RMSE values and a consistent negative bias (i.e. underestimation) in estimates of annual river loads across all sampling frequencies. The RC method resulted in the lowest RMSE for TN, TP and TSS at monthly sampling frequency. Yet, RC highly overestimated the loads for parameters that showed dilution effect such as NO-N and SIN. The FW and RE methods gave similar results, and there was no essential improvement in using RE over FW. In general, FW and RE performed better than FS in terms of bias, but FS performed slightly better than FW and RE in terms of RMSE for most of the water quality parameters (DRP, TP, TN and TSS) using a monthly sampling frequency. We found no significant decrease in RMSE values for estimates of NON, SIN, TN and DRP loads when the sampling frequency was increased from monthly to fortnightly. The bias and RMSE values in estimates of TP and TSS loads (estimated by FW, RE and FS), however, showed a significant decrease in the case of weekly or 2-day sampling. This suggests potential for a higher sampling frequency during flow peaks for more precise and accurate estimates of annual river loads for TP and TSS, in the study river and other similar conditions.