Water use in a sugarcane plantation

The evapotranspiration (E) from a sugarcane plantation in the southeast Brazil was measured by the eddy‐covariance method during two consecutive cycles. These represented the second (393 days) and third year (374 days) re‐growth (ratoon). The total E in the first cycle was 829 mm, accounting for 69% of rainfall, whereas in the second cycle, it was 690 mm, despite the total rainfall (1353 mm) being 13% greater. The ratio of E to available energy, the evaporative fraction, exhibited a smaller variation between the first and second cycles: 0.58 and 0.51, respectively. The estimated interception losses were 88 and 90 mm, respectively, accounting for approximately 7% of the total rainfall. The sugarcane yield in the second cycle (61.5 ± 4.0 t ha^?1) was 26% lower than the first cycle, as well as lower than the regional average for the third ratoon (76 t ha^?1). The below average yield was associated with less available soil water at the beginning of the cycle, with the amount of rainfall recorded during the first 120 days of re‐growth in the second cycle being 16% of that recorded in the first (203 mm).     

Population dynamics of the bird cherry‐oat aphid,Rhopalosiphum padi (L.), during the autumn and winter: a modelling approach

1 A simulation model was developed to investigate the inter‐relationship of factors influencing the population dynamics of the bird cherry‐oat aphid (Rhopalosiphum padi (L.)) in barley crops during the autumn and winter. 2 The model incorporated algorithms describing alate immigration, development and survival of adults and nymphs, fecundity and morph determination in newly born nymphs. 3 The model was validated against pest outbreaks in barley fields in south‐east England. 4 It simulated accurately the size of the outbreaks with predictions of peak aphid populations within 20% of the observed in all but one case. Similarly, all but one of the year‐sowing date combination predictions of timing of peak abundance fell within 14 days of the observed. 5 A sensitivity analysis of the model highlighted the relative importance of various population processes in determining simulated aphid population dynamics; decreasing mortality rates of apterous nymphs by as little as 5% over the autumn and winter increased peak densities by as much as 60‐fold, whereas increasing daily temperatures by only 1 °C more than doubled peak aphid abundance. 6 The model identified our understanding of the mechanisms of aphid mortality as a limiting factor in the accurate prediction of R. padi outbreaks in the field.     

Simulation of management and soil interactions impacting SOC dynamics in sugarcane using the CENTURY Model

Newer methods of management and harvesting of sugarcane are being considered to improve soil and water conservation in Brazil. Our aim in this study was to evaluate soil C dynamics under sugarcane cultivation as influenced by the use of conservation management, using measurements from four different management systems and land use histories, i.e. conventional management with preharvest burning, no burning with residue retention and two systems without burning plus additional organic amendments. Field sites also differed in terms of soil texture. We compared field measurements of soil C stocks, ¹³C and microbial biomass with simulated results from the Century ecosystem model for each of the sites and management histories. We also did long-term simulations of the management treatments and sites to approximate steady-state SOC levels, to explore potential management-induced differences in SOC stocks and interactions with soil texture. The model accurately represented treatment and site differences for total SOC stocks, in which SOC stocks were strongly affected by both rates of organic matter input to soil and soil clay content. However, the model tended to underestimate the relative contribution of sugarcane-derived C to total SOC for sites with high residue and external organic matter amendments. Measured microbial biomass C across the sites was closely aligned with relative amounts of organic matter input but did not appear to be strongly affected by soil texture, whereas the model predicted that both texture and organic matter input rate would impact microbial biomass C. Long-term simulations of the conservation management alternatives suggested that SOC stocks could be maintained at or above levels in the original native Cerradão vegetation, whereas conventional practices using residue burning would result in a reduction of SOC to ca. 60% of native levels. Our results support the use of the CENTURY model as an aid to assess the impacts of different soil management practices on SOC stocks under sugarcane in Brazil.     

ORCHIDEE‐STICS,a process‐based model of sugarcane biomass production: calibration of model parameters governing phenology

Agro‐Land Surface Models (agro‐LSM) combine detailed crop models and large‐scale vegetation models (DGVMs) to model the spatial and temporal distribution of energy, water, and carbon fluxes within the soil–vegetation–atmosphere continuum worldwide. In this study, we identify and optimize parameters controlling leaf area index (LAI) in the agro‐LSM ORCHIDEE‐STICS developed for sugarcane. Using the Morris method to identify the key parameters impacting LAI, at eight different sugarcane field trial sites, in Australia and La Reunion island, we determined that the three most important parameters for simulating LAI are (i) the maximum predefined rate of LAI increase during the early crop development phase, a parameter that defines a plant density threshold below which individual plants do not compete for growing their LAI, and a parameter defining a threshold for nitrogen stress on LAI. A multisite calibration of these three parameters is performed using three different scoring functions. The impact of the choice of a particular scoring function on the optimized parameter values is investigated by testing scoring functions defined from the model‐data RMSE, the figure of merit and a Bayesian quadratic model‐data misfit function. The robustness of the calibration is evaluated for each of the three scoring functions with a systematic cross‐validation method to find the most satisfactory one. Our results show that the figure of merit scoring function is the most robust metric for establishing the best parameter values controlling the LAI. The multisite average figure of merit scoring function is improved from 67% of agreement to 79%. The residual error in LAI simulation after the calibration is discussed.     

Modeling spatial and dynamic variation in growth,yield, and yield stability of the bioenergy crops Miscanthus × giganteus and Panicum virgatum across the conterminous United States

C₄ perennial grasses are being considered as environmentally and economically sustainable high yielding bioenergy feedstocks. Temporal and spatial variation in yield across the conterminious United States is uncertain due to the limited number of field trials. Here, we use a semi‐mechanistic dynamic crop growth and production model to explore the potential of Miscanthus × giganteus (Greef et. Deu.) and Panicum virgatum L. across the conterminous United States. By running the model for 32 years (1979–2010), we were able to estimate dry biomass production and stability. The maximum rainfed simulated end‐of‐growth‐season harvestable biomass for M. × giganteus was ca. 40 Mg ha^?1 and ca. 20 Mg ha^?1 for P. virgatum. In addition, regions of the southeastern United States were identified as promising due to their high potential production and stability and their relative advantage when compared with county‐level maize biomass production. Regional and temporal variation was most strongly influenced by precipitation and soil water holding capacity. Miscanthus × giganteus was on average 2.2 times more productive than P. virgatum for locations where yields were ≥10 Mg ha^?1. The predictive ability of the model for P. virgatum was tested with 30 previously published studies covering the eastern half of the United States and resulted in an index of agreement of 0.71 and a mean bias of only ?0.62 Mg ha^?1 showing that, on average, the model tended to only slightly overestimate productivity. This study provides with potential production and variability which can be used for regional assessment of the suitability of dedicated bioenergy crops.     

Effects of single and multiple herbivory by host and non‐host caterpillars on the attractiveness of herbivore‐induced volatiles of sugarcane to the generalist parasitoid Cotesia flavipes

It is well known that parasitoids are attracted to volatiles emitted by host‐damaged plants; however, this tritrophic interaction may change if plants are attacked by more than one herbivore species. The larval parasitoid Cotesia flavipesCameron (Hymenoptera: Braconidae) has been used intensively in Brazil to control the sugarcane borer, Diatraea saccharalisFabricius (Lepidoptera: Pyralidae) in sugarcane crops, where Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae), a non‐stemborer lepidopteran, is also a pest. Here, we investigated the ability of C. flavipes to discriminate between an unsuitable host (S. frugiperda) and a suitable host (D. saccharalis) based on herbivore‐induced plant volatiles (HIPVs) emitted by sugarcane, and whether multiple herbivory (D. saccharalis feeding on stalk + S. frugiperda feeding on leaves) in sugarcane affected the attractiveness of HIPVs to C. flavipes. Olfactometer assays indicated that volatiles of host and non‐host‐damaged plants were attractive to C. flavipes. Even though host‐ and non‐host‐damaged plants emitted considerably different volatile blends, neither naïve nor experienced wasps discriminated suitable and unsuitable hosts by means of HIPVs emitted by sugarcane. With regard to multiple herbivory, wasps innately preferred the odor blend emitted by sugarcane upon non‐host + host herbivory over host‐only damaged plants. Multiple herbivory caused a suppression of some volatiles relative to non‐host‐damaged sugarcane that may have resulted from the unaltered levels of jasmonic acid in host‐damaged plants, or from reduced palatability of host‐damaged plants to S. frugiperda. In conclusion, our study showed that C. flavipes responds to a wide range of plant volatile blends, and does not discriminate host from non‐host and non‐stemborer caterpillars based on HIPVs emitted from sugarcane. Moreover, we showed that multiple herbivory by the sugarcane borer and fall armyworm increases the attractiveness of sugarcane plants to the parasitoids.     

Evaluation of Agricultural Production Systems Simulator as yield predictor of Panicum virgatum and Miscanthus x giganteus in several US environments

Simulation models for perennial energy crops such as switchgrass (Panicum virgatum L.) and Miscanthus (Miscanthus x giganteus) can be useful tools to design management strategies for biomass productivity improvement in US environments. The Agricultural Production Systems Simulator (APSIM) is a biophysical model with the potential to simulate the growth of perennial crops. APSIM crop modules do not exist for switchgrass and Miscanthus, however, re‐parameterization of existing APSIM modules could be used to simulate the growth of these perennials. Our aim was to evaluate the ability of APSIM to predict the dry matter (DM) yield of switchgrass and Miscanthus at several US locations. The Lucerne (for switchgrass) and Sugarcane (for Miscanthus) APSIM modules were calibrated using data from four locations in Indiana. A sensitivity analysis informed the relative impact of changes in plant and soil parameters of APSIM Lucerne and APSIM Sugarcane modules. An independent dataset of switchgrass and Miscanthus DM yields from several US environments was used to validate these re‐parameterized APSIM modules. The re‐parameterized modules simulated DM yields of switchgrass [0.95 for CCC (concordance correlation coefficient) and 0 for SB (bias of the simulation from the measurement)] and Miscanthus (0.65 and 0% for CCC and SB, respectively) accurately at most locations with the exception of switchgrass at southern US sites (0.01 for CCC and 2% for SB). Therefore, the APSIM model is a promising tool for simulating DM yields for switchgrass and Miscanthus while accounting for environmental variability. Given our study was strictly based on APSIM calibrations at Indiana locations, additional research using more extensive calibration data may enhance APSIM robustness.     

Climate trends account for stalled wheat yields in Australia since 1990

Global food security requires that grain yields continue to increase to 2050, yet yields have stalled in many developed countries. This disturbing trend has so far been only partially explained. Here, we show that wheat yields in Australia have stalled since 1990 and investigate the extent to which climate trends account for this observation. Based on simulation of 50 sites with quality weather data, that are representative of the agro‐ecological zones and of soil types in the grain zone, we show that water‐limited yield potential declined by 27% over a 26 year period from 1990 to 2015. We attribute this decline to reduced rainfall and to rising temperatures while the positive effect of elevated atmospheric CO₂ concentrations prevented a further 4% loss relative to 1990 yields. Closer investigation of three sites revealed the nature of the simulated response of water‐limited yield to water availability, water stress and maximum temperatures. At all three sites, maximum temperature hastened time from sowing to flowering and to maturity and reduced grain number per m² and average weight per grain. This 27% climate‐driven decline in water‐limited yield is not fully expressed in actual national yields. This is due to an unprecedented rate of technology‐driven gains closing the gap between actual and water‐limited potential yields by 25 kg ha^?1 yr^?1 enabling relative yields to increase from 39% in 1990 to 55% in 2015. It remains to be seen whether technology can continue to maintain current yields, let alone increase them to those required by 2050.     

The Brazilian experience of sugarcane ethanol industry

Biomass has gained prominence in the last few years as one of the most important renewable energy sources. In Brazil, a sugarcane ethanol program called ProAlcohol was designed to supply the liquid gasoline substitution and has been running for the last 30 yr. The federal government’s establishment of ProAlcohol in 1975 created the grounds for the development of a sugarcane industry that currently is one of the most efficient systems for the conversion of photosynthate into different forms of energy. Improvement of industrial processes along with strong sugarcane breeding programs brought technologies that currently support a cropland of 7 million hectares of sugarcane with an average yield of 75 tons/ha. From the beginning of ProAlcohol to the present time, ethanol yield has grown from 2,500 to around 7,000 l/ha. New technologies for energy production from crushed sugarcane stalk are currently supplying 15% of the electricity needs of the country. Projections show that sugarcane could supply over 30% of Brazil’s energy needs by 2020. In this review, we briefly describe some historic facts of the ethanol industry, the role of sugarcane breeding, and the prospects of sugarcane biotechnology     

How do various maize crop models vary in their responses to climate change factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO₂], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly ?0.5 Mg ha^?1 per °C. Doubling [CO₂] from 360 to 720 μmol mol^?1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO₂] among models. Model responses to temperature and [CO₂] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.     

Multimodel ensembles of wheat growth: many models are better than one

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24–38% for the different end‐of‐season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in‐season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e‐mean) or median (e‐median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e‐median ranked first in simulating measured GY and third in GPC. The error of e‐mean and e‐median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.     

Projected climate impacts to South African maize and wheat production in 2055: a comparison of empirical and mechanistic modeling approaches

Crop model‐specific biases are a key uncertainty affecting our understanding of climate change impacts to agriculture. There is increasing research focus on intermodel variation, but comparisons between mechanistic (MMs) and empirical models (EMs) are rare despite both being used widely in this field. We combined MMs and EMs to project future (2055) changes in the potential distribution (suitability) and productivity of maize and spring wheat in South Africa under 18 downscaled climate scenarios (9 models run under 2 emissions scenarios). EMs projected larger yield losses or smaller gains than MMs. The EMs’ median‐projected maize and wheat yield changes were ?3.6% and 6.2%, respectively, compared to 6.5% and 15.2% for the MM. The EM projected a 10% reduction in the potential maize growing area, where the MM projected a 9% gain. Both models showed increases in the potential spring wheat production region (EM = 48%, MM = 20%), but these results were more equivocal because both models (particularly the EM) substantially overestimated the extent of current suitability. The substantial water‐use efficiency gains simulated by the MMs under elevated CO₂ accounted for much of the EM?MM difference, but EMs may have more accurately represented crop temperature sensitivities. Our results align with earlier studies showing that EMs may show larger climate change losses than MMs. Crop forecasting efforts should expand to include EM?MM comparisons to provide a fuller picture of crop–climate response uncertainties.     

A new cost‐distance model for human accessibility and an evaluation of accessibility bias in permanent vegetation plots in Great Smoky Mountains National Park,USA

Question: Can a new cost‐distance model help us to evaluate the potential for accessibility bias in ecological observations? How much accessibility bias is present in the vegetation monitoring plots accumulated over the last three decades in Great Smoky Mountains National Park? Location: Great Smoky Mountains National Park, North Carolina and Tennessee, USA. Methods: Distance, slope, stream crossings, and vegetation density were incorporated into a least‐cost model of energetic expenditure for human access to locations. Results: Estimated round‐trip energy costs for the park ranged from 0 to 1.62 × 10⁵ J kg^?1. The estimated round‐trip energetic expenditure for the surveys ranged from 53 to 1.51 × 10⁵ J kg^?1. Their distribution was more accessible than the random expectation. Ten (17%) of the vegetation types in the park are significantly under‐sampled relative to their area, and 16 (29%) are over‐sampled. Plots in 18 of the 40 vegetation types exhibited a significant positive correlation with accessibility. Conclusions: The least‐cost model is an improvement over previous attempts to quantify accessibility. The bias in plot locations suggests using a least‐cost model to test for bias in cases in which human accessibility is confounded with other sources of ecosystem variation.     

Assessment of Genetic Diversity,Micromorphology and Antimicrobial Activity in Nepeta cataria L.

The mosquito repellent Nepetalactone rich Nepeta cataria L. (catmint) plant has a variety of therapeutic and industrial potential. Reports on the genetic diversity of N. cataria germplasm are minimal globally and need attention for adding a new variety into commercial cultivation. The present study, therefore, assessed the genetic diversity among thirteen half-sib genotypes of N. cataria using agro economic and phytochemical traits. The experimental set has shown substantial variation for agro economic traits studied. Among all the studied populations, fresh herb-based essential oil content ranged from 0.1 % to 0.3 %, with a grand mean of 1.67 %. However, the estimated oil yield ranged from 44.4 kg/h to 120.73 kg/h with an average of 71.34 kg/h. Among the eleven phytochemical constituents detected in different concentrations in the essential oil of experimental sets, 4aα,7α,7aα-Nepetalactone (67.9–87.5 %) constituted the significant proportion of essential oil. Altogether, based on mean comparison, the population NC8 was found to be promising for estimated oil yield and 4aα,7α,7aα-Nepetalactone content. The greater heritability estimates (h²bs) and genetic advance as percent of mean (GAM) were observed for important economic parameters, i. e., oil content, herb yield, and oil yield. The cluster analysis revealed the least interactions between various agro economic and phytochemical variables. The microscopic study of trichome showed a positive correlation of abaxial leaf surface with essential oil content. The promising antimicrobial potential of catmint oil was also observed against human health-related pathogens. The results infer from our study provide valuable insight for genetic improvement and product development in the catmint germplasm.     

The impact of agro‐pastoral abandonment on the Rock Partridge Alectoris graeca in the Apennines

Human depopulation of rural mountain areas and the consequent abandonment of traditional land management are among the greatest driving forces behind changes in mountain ecosystems in Western Europe. Tree and shrub encroachment lead to an increase in landscape matrix uniformity and habitat fragmentation. For some animal species, this represents an unusual case of habitat loss caused by secondary succession. The animal species associated with this agro‐pastoral habitat may suffer from decreased connectivity as a consequence. The Rock Partridge Alectoris graeca is a species endemic to European mountains that represents a model for investigating the impact of habitat loss. We compared the habitat suitability of the Apennine Rock Partridge prior to abandonment of traditional agro‐pastoral activities by aerial photography with the current landscape, in order to investigate the effect of secondary succession on the distribution and viability of the species. We assessed the historical distribution (c. 1900–1950) by quantifying anecdotal evidence from interviews, and the current distribution (2005) from survey data. We applied ecological niche factor analysis and connectivity approaches to evaluate change in habitat suitability over this time scale. Moreover, to quantify landscape connectivity, we evaluated the relative importance of each patch in the two periods. Results indicated that to maintain a viable population in the Apennines, the species requires an ensemble of ecological conditions considerably different from the current situation. We observed a drastic decrease in connectivity as a result of a reduction in numbers and size of high suitability patches. This is most probably the primary cause of the current decline of the Rock Partridge population in the Apennines.     

Comparison of three bioelectrical impedance methods with DXA in overweight and obese men

Objective: To compare bioelectrical impedance analysis (BIA) of body composition using three different methods against DXA in overweight and obese men. Research Methods and Procedures: Forty‐three healthy overweight or obese men (ages 25 to 60 years; BMI, 28 to 43 kg/m²) underwent BIA assessment of body composition using the ImpediMed SFB7 (version 6; ImpediMed, Ltd., Eight Mile Plains, Queensland, Australia) in multifrequency mode (Imp‐MF) and DF50 single‐frequency mode (Imp‐SF) and the Tanita UltimateScale (Tanita Corp., Tokyo, Japan). Validity was assessed by comparison against DXA using linear regression and limits of agreement analysis. Results: All three BIA methods showed good relative agreement with DXA [Imp‐MF: fat mass (FM), r² = 0.81; fat‐free mass (FFM), r² = 0.81; percentage body fat (BF%), r² = 0.69; Imp‐SF: FM, r² = 0.65; FFM, r² = 0.76; BF%, r² = 0.40; Tanita: BF%, r² = 0.44; all p < 0.001]. Absolute agreement between DXA and Imp‐MF was poor, as indicated by a large bias and wide limits of agreement (bias, ±1.96 standard deviation; FM, ?6.6 ± 7.7 kg; FFM, 8.0 ± 7.1 kg; BF%, ?7.0 ± 6.6%). Imp‐SF and Tanita exhibited a smaller bias but wide limits of agreement (Imp‐SF: FM, ?1.1 ± 8.5 kg; FFM, 2.5 ± 7.9 kg; BF%, ?1.7 ± 7.3% Tanita: BF%, 1.2 ± 9.5%). Discussion: Compared with DXA, Imp‐MF produced large bias and wide limits of agreement, and its accuracy estimating body composition in overweight or obese men was poor. Imp‐SF and Tanita demonstrated little bias and may be useful for group comparisons, but their utility for assessment of body composition in individuals is limited.     

A potato model intercomparison across varying climates and productivity levels

A potato crop multimodel assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low‐input (Chinoli, Bolivia and Gisozi, Burundi)‐ and high‐input (Jyndevad, Denmark and Washington, United States) management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with interannual variability, and predictions for all agronomic variables were significantly different from one model to another (P < 0.001). Uncertainty averaged 15% higher for low‐ vs. high‐input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41% and 23% for yield and ET, respectively, as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100‐ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for nonirrigated sites). Differences in predictions due to model representation of light utilization were significant (P < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.     

On Estimating the Relationship between Longitudinal Measurements and Time‐to‐Event Data Using a Simple Two‐Stage Procedure

Summary Ye, Lin, and Taylor (2008, Biometrics 64 , 1238–1246) proposed a joint model for longitudinal measurements and time‐to‐event data in which the longitudinal measurements are modeled with a semiparametric mixed model to allow for the complex patterns in longitudinal biomarker data. They proposed a two‐stage regression calibration approach that is simpler to implement than a joint modeling approach. In the first stage of their approach, the mixed model is fit without regard to the time‐to‐event data. In the second stage, the posterior expectation of an individual's random effects from the mixed‐model are included as covariates in a Cox model. Although Ye et al. (2008) acknowledged that their regression calibration approach may cause a bias due to the problem of informative dropout and measurement error, they argued that the bias is small relative to alternative methods. In this article, we show that this bias may be substantial. We show how to alleviate much of this bias with an alternative regression calibration approach that can be applied for both discrete and continuous time‐to‐event data. Through simulations, the proposed approach is shown to have substantially less bias than the regression calibration approach proposed by Ye et al. (2008) . In agreement with the methodology proposed by Ye et al. (2008) , an advantage of our proposed approach over joint modeling is that it can be implemented with standard statistical software and does not require complex estimation techniques.     

Regional material flow behaviors of agro‐food processing craft villages in Red River Delta,Vietnam

The economic reform “??i M?i” in 1986 has rapidly increased the number of craft villages in Vietnam, especially in the Red River Delta (RRD) leading to environmental degradation. This article presents an assessment of environmental and resource issues of agro‐Food Processing Craft Villages (FPCVs) in RRD using a refined approach to material flow analysis focusing on consistent quantification of uncertainty with particular attention to secondary and empirical data that are often faced in material flow analyses in transition economies. Material flows of agro‐Food Processing including eight types of production were examined and linked to activities of private Households, Rice Cultivation, and Pig Farming in a model called Red River Delta. Materials investigated were Goods (i.e., total materials), organic carbon (org.C), nitrogen (N), and phosphorus (P). The findings reveal material cycles are almost entirely open, that is, the materials used in FPCVs do not recycle within the region. From ～10.5 million tons/year of imported Goods used for agro‐Food Processing, final products and utilized materials account for minor fractions (～5%, by weight). Conversely, the majority (88%) is directly discharged. Materials accumulated as stocks represent 1% of Goods (100,000 tons/year), 21% of org.C (～34,000 tons/year), 42% of N (～1,300 tons/year), and 57% of P (～300 tons/year), whose substance concentrations vastly exceed natural resilience capacities. Although agro‐Food Processing accounts for negligible material shares in Red River Delta, FPCVs pollution is severe at local levels due to the location of home‐based production. Several options for closing material loops at various system scales are recommended for environmental and resource management of FPCVs. The material flow analysis results provide a database that may be used as a decision support tool for production establishments in craft villages and relevant authorities in setting priorities on environmental planning and resource management. This article met the requirements for a gold – silver JIE data openness badge described at http://jie.click/badges .     

Diagnosing and assessing uncertainties of terrestrial ecosystem models in a multimodel ensemble experiment: 1. Primary production

We conducted an ensemble modeling exercise using the Terrestrial Observation and Prediction System (TOPS) to evaluate sources of uncertainty in carbon flux estimates resulting from structural differences among ecosystem models. The experiment ran public‐domain versions of biome‐bgc, lpj, casa , and tops‐bgc over North America at 8 km resolution and for the period of 1982–2006. We developed the Hierarchical Framework for Diagnosing Ecosystem Models (HFDEM) to separate the simulated biogeochemistry into a cascade of three functional tiers and sequentially examine their characteristics in climate (temperature–precipitation) and other spaces. Analysis of the simulated annual gross primary production (GPP) in the climate domain indicates a general agreement among the models, all showing optimal GPP in regions where the relationship between annual average temperature (T, °C) and annual total precipitation (P, mm) is defined by P=50T+500. However, differences in simulated GPP are identified in magnitudes and distribution patterns. For forests, the GPP gradient along P=50T+500 ranges from ～50 g C yr^?1 m^?2 °C^?1 (casa ) to ～125 g C yr^?1 m^?2 °C^?1 (biome‐bgc ) in cold/temperate regions; for nonforests, the diversity among GPP distributions is even larger. Positive linear relationships are found between annual GPP and annual mean leaf area index (LAI) in all models. For biome‐bgc and lpj , such relationships lead to a positive feedback from LAI growth to GPP enhancement. Different approaches to constrain this feedback lead to different sensitivity of the models to disturbances such as fire, which contribute significantly to the diversity in GPP stated above. The ratios between independently simulated NPP and GPP are close to 50% on average; however, their distribution patterns vary significantly between models, reflecting the difficulties in estimating autotrophic respiration across various climate regimes. Although these results are drawn from our experiments with the tested model versions, the developed methodology has potential for other model exercises.