Simulating future Food Value Chain components through the integration of biophysical and techno-economic spatial models

The performance of the Agricultural Production Systems Simulator (APSIM) lucerne (Medicago sativa) model was assessed by comparing calculations from APSIM with measured (or observed) data from 9 sites in Western Australia. This comparison was also to obtain new insights into lucerne production and the effect of lucerne on the water balance in this environment. APSIM accounted adequately for the temporal change in above-ground biomass production and the plant-available water (PAW) for most of the sites. The root mean square deviation (RMSD) for biomass was 1.3 t/ha for the mean observed biomass of 4.17 t/ha. The RMSD for PAW was 16 mm to a depth of 1.6–2.1 m, with the mean observed PAW of 50 mm. The good prediction of PAW was partly because critical soil parameters used in APSIM were derived from the soil water content measurements. APSIM also adequately (within the standard error) estimated evapotranspiration (Et) and drainage below the root-zone, which was measured at 2 of the sites. The analysis supports previous findings with lucerne that increased storage of carbohydrates in root reserves occurs in autumn and winter. Given that APSIM performed adequately when calibrated, it was used to simulate Et and drainage for the 7 sites in which measurements were not taken. Et in all 3 years of lucerne and drainage in Year 1 were related to the amount of rainfall. Fifty-one percent of rainfall above 230 mm was lost as water excess in Year 1 (R2 = 0.68). Drainage in Year 3 was less than drainage in Year 1, confirming previous studies that established lucerne can reduce drainage.

Field experiments are subjected to spatial variability due to factors such as soil moisture, fertility, pH, and structure, as well as the pressure of diseases and pests. Soybean yields are highly variable across fields. Controlling spatial variability could decrease the risk of erroneous inferences in breeding trials. This study aims at evaluating the spatial variability of furrow-irrigated soybean for seed yield, wilting, and maturity under four different irrigation levels. The field experiment was conducted in four environmzents (location-year combination). A total of 165 soybean lines of similar relative maturity (maturity group 5) along with commercial checks were planted in an augmented strip plot design. Irrigation treatment decisions were triggered using an atmometer based on a threshold at a designated growth stage. Data were analyzed via Analysis of Variance as a linear mixed model using a blocking structure (block model) and spatial covariances using range and column. Two different spatial models were used: exponential and Gaussian. Results showed that the spatial models displayed better data fitting (lower AIC and/or BIC) than the block model in each different irrigation level across different environments and traits. Indeed, genotype ranking for seed yield was different between the block model and the best spatial model, suggesting that spatial adjustment may be necessary for soybean breeding operations under furrow irrigation. Further validation in a breeding yield trial demonstrated similar results of the effectiveness in terms of AIC and/or BIC of the spatial model compared to the block model for soybean seed yield.

Alfalfa is an important perennial forage crop in Idaho supporting dairy and cattle industries that is typically grown in the same field for as many as 4 years. Alfalfa stands of different ages were subjected to screening for viruses using high-throughput sequencing and RT-PCR. The two most common viruses found were alfalfa mosaic virus and bean leafroll virus, along with Medicago sativa amalgavirus, two alphapartitiviruses, and one deltapartitivirus. Additionally, a new flavi-like virus with an unusual genome organization was discovered, dubbed Snake River alfalfa virus (SRAV). The 11,745 nt, positive-sense (+) RNA genome of SRAV encodes a single 3835 aa polyprotein with only two identifiable conserved domains, an RNA-dependent RNA polymerase (RdRP) and a predicted serine protease. Notably, unlike all +RNA virus genomes in the similar size range, the SRAV polyprotein contained no predicted helicase domain. In the RdRP phylogeny, SRAV was placed inside the flavi-like lineage as a sister clade to a branch consisting of hepaci-, and pegiviruses. To the best of our knowledge, SRAV is the first flavi-like virus identified in a plant host. Although commonly detected in alfalfa crops in southern Idaho, SRAV sequences were also amplified from thrips feeding in alfalfa stands in the area, suggesting a possible role of Frankliniella occidentalis in virus transmission.

In studies assessing the prospects for a hemp industry, as well as in longer term research activities, the use of a hemp simulation model to complement the more traditional agronomic field trials would offer a number of potential advantages. In addition to being cost and labour intensive, field trials with hemp have political, social and security implications. With these implications in mind, a simulation model that captures the growth and development processes of hemp in response to management, genotypic, soil and climate factors, has the potential to increase research efficiency. The model could be used to assess the need, extent and nature of field trials, to help interpret field trial results, and to investigate temporal and spatial variability in selected crop responses. This paper describes a hemp crop growth and development model (APSIM-Hemp) and its validation against an independent dataset. The model was developed as a crop module within the framework of the larger systems model, Agricultural Production Systems sIMulator (APSIM), to extend the capability to encompass the agricultural system in which hemp is grown. APSIM-Hemp incorporates relationships developed in the previous papers in this series relating to pre- and post-emergent phenology and leaf area production. Other parameters relating to biomass partitioning, biomass production, water uptake and nitrogen uptake were derived from separate field studies and selected references. APSIM-Hemp adequately predicted phenology, leaf area and biomass production for the cultivar Kompolti at Forthside in north-western Tasmania, for a dataset comprised of results from trials conducted over 3 seasons and including treatments of sowing date, irrigation regime and plant density. Although performing well against this independent dataset, the performance of the model needs to be further validated over a range of other soil, climate and management conditions in order to assess its broader predictive capability. Notwithstanding these limitations, the sound basis of a model for simulating the growth and development of hemp has been developed.

The main purpose of this chapter is to review the parametric spatial econometric models that can be applied to regional economics. Spatial econometric methods are based on regression analysis applied to cases where spatial interactions and spatial structures are fundamental characteristics of the process under discussion. The review presented here outlines the basic terminology, the spatial data dependence, the specification of spatial effects, and some basic spatial regression models, i.e., the spatial autoregressive (SAR) model (or spatial lag model), the spatial error model (SEM), the spatial Durbin model (SDM) and the general spatial models—SAC and SARMA. The maximum likelihood estimation for SAR and SEM models it is also presented with some detail.In the context of the European Union, we should emphasize several empirical works in the particular areas of urban economics, economic growth and productivity, and studies dealing with agglomeration and externalities (spillovers). We provide a brief survey of some of the results obtained in these particular areas.

Spatial variability and model uncertainty are considered the major sources of geotechnical uncertainty. The primary objective of this paper is to provide slope stability investigators with a robust reliability framework that takes into consideration the combined uncertainty of spatial variability and model uncertainty. To achieve this objective, a quantification of the model uncertainty of common slope stability models is conducted by assembling and analyzing a database of historical failures of slopes. The database is also used to investigate the possibility of a lower-bound factor of safety and its impact on the reliability of slopes. It is concluded that both spatial variability and model uncertainty in the undrained shear strength of clays have a direct effect on the mean of the factor of safety and its coefficient of variation. Moreover, it is found that the lower-bound factor of safety can cause a significant increase in the calculated reliability for an undrained slope

Comparing spatial distribution modelling of fisheries data with single-area or spatially-explicit integrated population models, a case study of toothfish in the Ross Sea region

This study aimed at quantifying the temporal and spatial variability in sand erosion and deposition over a coastal dune field in Israel. These were measured monthly over 2 years using 315 erosion pins over four transects that were placed perpendicular to the coastline. Vegetation cover was estimated based on aerial photographs and Landsat satellite images, whereas the relative height was based on a digital elevation model. These variables were calculated for the area upwind (south west) of the erosion pins, at various lengths, ranging from 15 to 400 m. Nine geomorphologic units were defined, five related to active units, and four to stabilized units. In active units at least 65% of the temporal variance in the annual absolute changes in sand level was explained by the index of Resultant Drift Potential, with most of the sand movement occurring during winter storms. Local rainfall had no apparent impact on sand mobility, due to the low coincidence of sand carrying winds and rainfall in Israel during the passage of frontal cyclones. As for the spatial variables, only a weak correlation was found between sand mobility with the distance from the coastline (R2 = 18%). Rather, sand erosion and deposition were influenced by vegetation cover and the relative height of an area of 100–200 m upwind. The values of Soil Adjusted Vegetation Index were significantly negatively correlated with annual absolute changes (R2 = 40%), whereas the relative height was significantly positively correlated (R2 = 36%). Applying a multiple regression model, 68% of the spatial variability in sand mobility was explained. The resulting map of sand activity clearly shows that at this stage of the stabilization process, most of the dunes are now disconnected, and movement of sand grains from the beach or between the dunes, is very limited. These methods can be applied into spatial and temporal models of sand mobility, thus assessing the impact of different management practices on coastal dunes.

Summary 1. Reductions in resource availability, associated with land‐use change and agricultural intensification in the UK and Europe, have been linked with the widespread decline of many farmland bird species over recent decades. However, the underlying ecological processes which link resource availability and population trends are poorly understood. 2. We construct a spatial depletion model to investigate the relationship between the population persistence of granivorous birds within the agricultural landscape and the temporal dynamics of stubble field availability, an important source of winter food for many of those species. 3. The model is capable of accurately predicting the distribution of a given number of finches and buntings amongst patches of different stubble types in an agricultural landscape over the course of a winter and assessing the relative value of different landscapes in terms of resource availability. 4. Sensitivity analyses showed that the model is relatively robust to estimates of energetic requirements, search efficiency and handling time but that daily seed survival estimates have a strong influence on model fit. Understanding resource dynamics in agricultural landscapes is highlighted as a key area for further research. 5. There was a positive relationship between the predicted number of bird days supported by a landscape over‐winter and the breeding population trend for yellowhammer Emberiza citrinella, a species for which survival has been identified as the primary driver of population dynamics, but not for linnet Carduelis cannabina, a species for which productivity has been identified as the primary driver of population dynamics. 6. Synthesis and applications. We believe this model can be used to guide the effective delivery of over‐winter food resources under agri‐environment schemes and to assess the impacts on granivorous birds of changing resource availability associated with novel changes in land use. This could be very important in the future as farming adapts to an increasingly dynamic trading environment, in which demands for increased agricultural production must be reconciled with objectives for environmental protection, including biodiversity conservation.

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 06:215-225 (1996) - DOI: https://doi.org/10.3354/cr006215 Spatial modeling and interpolation of monthly temperature using kriging Holdaway MR Estimates of the climatic record at forest plot locations may be useful in studying how forests will respond to future climatic change. Kriging was applied to the spatial interpolation of monthly temperature records in the forested regions of Minnesota in the north central United States. Monthly empirical variograms, averaged over 90 yr, were modeled with Gaussian or linear models, and ordinary kriging was applied to interpolate the data. Anisotropies were found in the winter months, suggesting the presence of a large-scale regional trend. Structural analysis of mean monthly temperature revealed: (1) a broad regional component to the variation, changing systematically by month, which was estimated by a linear function of latitude and longitude and (2) a lake effect (due to Lake Superior) varying in strength and sign by month. A detrending approach was tested to remove these effects and a modified approach based on only the lake effect trend was also tested. The cross validation technique was used to test the 3 models. The lake effect trend model was judged best in accounting for the influence of Lake Superior on nearby land areas. The study demonstrated that moderate trends in the data do not seriously degrade the applicability of ordinary kriging to the interpolation of monthly temperature. A temporal analysis revealed that, although there have been systematic changes in the spatial variability over the last century, using century-averaged variograms is not expected to decrease the accuracy and precision of the interpolations. Geostatistics · Residual kriging · Semivariogram modeling · Lake effect · Minnesota Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 06, No. 3. Publication date: June 13, 1996 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 1996 Inter-Research.

BackgroundCluster randomised trials (CRTs) often use geographical areas as the unit of randomisation, however explicit consideration of the location and spatial distribution of observations is rare. In many trials, the location of participants will have little importance, however in some, especially against infectious diseases, spillover effects due to participants being located close together may affect trial results. This review aims to identify spatial analysis methods used in CRTs and improve understanding of the impact of spatial effects on trial results.MethodsA systematic review of CRTs containing spatial methods, defined as a method that accounts for the structure, location, or relative distances between observations. We searched three sources: Ovid/Medline, Pubmed, and Web of Science databases. Spatial methods were categorised and details of the impact of spatial effects on trial results recorded.ResultsWe identified ten papers which met the inclusion criteria, comprising thirteen trials. We found that existing approaches fell into two categories; spatial variables and spatial modelling. The spatial variable approach was most common and involved standard statistical analysis of distance measurements. Spatial modelling is a more sophisticated approach which incorporates the spatial structure of the data within a random effects model. Studies tended to demonstrate the importance of accounting for location and distribution of observations in estimating unbiased effects.ConclusionsThere have been a few attempts to control and estimate spatial effects within the context of human CRTs, but our overall understanding is limited. Although spatial effects may bias trial results, their consideration was usually a supplementary, rather than primary analysis. Further work is required to evaluate and develop the spatial methodologies relevant to a range of CRTs.

A new methodology was developed to create statistical emulators from an existing complex plant growth simulation model, in order to provide simple models for predicting perennial pasture production based on meteorological data. The goal was to find emulators that could fit simulation data and provide reliable predictions of new simulation output. Four types of statistical emulators were compared -linear models, non-linear models, linear splines and cubic splines -which varied in complexity due to the use of different functional forms to fit main effects and different methods to determine model parameters. Two methods of model validation were used to ensure that the emulator selected provided good fits to the initial simulation output and gave adequate predictions of future simulation output. We illustrated the various techniques of model development and validation using the Agricultural Production Systems sIMulator (APSIM) plant growth model. The effects of rainfall amount, rainfall frequency, temperature and radiation on APSIM-generated biomass production of the perennial pasture species lucerne Medicago sativa were used as a case study. We conclude that, in this case, the best statistical emulator is the cubic spline. The novel approach illustrated here provides a rigorous, flexible and powerful means of developing and testing emulators for any complex simulation model.

Understanding the spatial effects of the synergistic development between agricultural carbon sequestration and emission reduction (ACSER) and food security (FS) is essential for promoting sustainable and high-quality agrarian development. Based on a constructed indicator system for ACSER and FS, this study measures the Coupling Coordination Degree (CCD) between the two in different grain functional areas of China from 2000 to 2023 using a modified coupling synergy model. Furthermore, a Spatial Durbin Model (SDM) is employed to explore the underlying driving mechanisms and spatial spillover effects of their synergistic relationship. (1) From 2000 to 2023, China's CCD between ACSER and FS exhibited a clear spatial gradient, higher in the north and lower in the south. Specifically, spatial clustering intensified in the main production and production-marketing areas but did not reach statistical significance, while the main marketing regions consistently exhibited a trend of spatial dispersion; (2) The spatial Durbin model analysis reveals that the CCD of ACSER and FS in China exhibits significant spatial spillover effects. Among the influencing factors, CO₂ uptake by major crops emerges as the primary driver of their synergistic development, while a higher proportion of cultivated land and increased pesticide and fertilizer use exert negative effects on both local and neighboring regions. (3)The study of regional heterogeneity shows that the CO₂ absorption of agricultural crops in the main production area promotes synergistic development; the synergistic mechanism of ACSER and FS in the production and marketing areas is more complicated, and resource mismatch is the main factor affecting the coupling and synergism of the two; The total power of agricultural machinery in the main marketing areas has a catalytic effect on the local synergy between ACSER and FS, while transregional transmission exhibits negative spillovers, highlighting resource allocation imbalances. Therefore, to promote the synergistic development of ACSER and FS, it is necessary to adopt region-specific measures based on spatial differences and to strengthen the synergistic effects among key factors within the agricultural system, to enhance resource allocation efficiency and system resilience.

Regional‐scale estimations of sorghum biomass production allow identification of optimum genotype×environment×management (G×E×M) combinations for bioenergy generation. The objective of this study was to determine the degree of contributions of G, E, and M toward variability in sorghum biomass in the United States. Using the Agricultural Production Systems sIMulator in a grid computing platform, biomass was simulated for irrigated and rainfed conditions for 30 years across the United States for four sorghum varietal types (grain—GS, sudangrass—SS, photosensitive—PS, and photo‐insensitive—PI). Simulated biomass was assessed by environments clustered using the sum of intercepted solar radiation (ir), mean of temperature stress factor (tp) and water stress factor (sw). Simulated biomass ranged from 5.8 t ha−1 (GS‐rainfed) to 27.5 t ha−1 (PI‐irrigated). Under high‐temperature environments (mean annual temperature = 25°C), rainfed biomass between 40 and 80 days after planting (DAP) was strongly correlated with sw (r = 0.64–0.86) and irrigated biomass with ir (r = 0.68–0.81). Under low‐temperature environments (mean annual temperature = 18°C) after 40 DAP, tp and ir had greater effects than sw (r = 0.55–0.82). Biomass variance was mainly explained by varietal type (50%–76%) in all environments×irrigation combinations, except in the high‐ and mid‐temperature environments under rainfed conditions where rainfall had the major effect (25%–45%). However, when mean temperature during the growing season decreased from 25°C (high environments) to 18°C (low environments), the contribution of mean temperature to biomass variance increased from 7% to 34% (rainfed) and from 4% to 36% (irrigated). Varietal type had the larger interactions with other factors independently of the environment and irrigation. We demonstrated a need to quantify (i) the main G×E×M drivers of biomass variability based on environmental stress factors and (ii) the variance contribution of these drivers on sorghum biomass. Our regional‐scale estimations are key inputs for future robust biomass projections of energy sorghum genotypes integrating G×E×M under climate change scenarios.

Transformation of agricultural landscapes under rapid urbanization: A threat to sustainability in Hang-Jia-Hu region, China