Spatial Yield Variability Research Articles

Mapping soil hydraulic conductivity and matric potential for water management of cranberry: Characterisation and spatial interpolation methods

Spatial interpolation methods are required for analysing the effects of soil hydraulic properties on irrigation management. This study was conducted to determine which interpolation methods are best suited to map these properties. During the summer of 2012 we mapped the spatial variability of soil physical properties, soil matric potential, water table depth and yield of two cranberry fields located near Quebec City, Canada. Three spatial interpolation methods, inverse distance weighting (IDW), thin plate splines (TPS) and kriging with external drift (KED), were compared by means of cross-validation. The best interpolation method for a given property was used to produce maps and perform HYDRUS 1D simulations for the purpose of irrigation management. Results show that even in highly constructed fields, such as for cranberries, spatial patterns of soil hydraulic properties exist. The TPS method was the best interpolation method based on the cross-validation analyses and generated maps. Spatial variability of crop yield showed a strong relationship with soil hydraulic properties and simulations suggest that irrigation can be reduced by 75% when accounting for the spatial variability of soil hydraulic properties.

Spatial Variability of Durum Wheat Yield as Related to Soil Parameters in an Organic Field

The yield in organic farming is generally much lower than its potential, which is due to its specificity. The objective of the present study was to quantify the yield spatial variation of wheat and relate it to soil parameters in an organic farm located in the north of the Negev Desert. Soil samples were gathered in a triangular grid at three time intervals. Yields were measured at 73 georeferenced points before the actual harvest. Several thematic maps of soil and yield parameters were produced using geographic information system and geostatistical methods. The strongest spatial correlation was found in the weight of 1000 grains and the weakest was in carbon flow. Temporal relationships were found between soil nitrate concentration, soil water content, and leaf area index. Wheat yield varied from 1.11 to 2.84 Mg ha−1 and this remarkable variation indicates that the spatial analysis of soil and yield parameters is significant in organic agriculture.

A simple assessment on spatial variability of rice yield and selected soil chemical properties of paddy fields in South China

Data related to the spatial variability of properties of paddy soil is critical for improving rice productivity and designing sustainable farming techniques. However, a systematic assessment focusing on the spatial variability of low-yield paddy soil has not been conducted in South China. In this context, 560 soil samples were collected across South China and analyzed for nine common chemical properties. Rice yield data was obtained by surveying farmers during soil sampling. Soil parameters and rice yield varied considerably throughout the study area and their coefficients of variation ranged from 17.3% to 74.2%. Experimental semivariograms were developed and a moderate spatial dependence was observed for all selected parameters. Distribution maps, derived by kriging interpolation, illustrated that these paddy fields were characterized by high concentrations of soil organic matter (SOM), total N (TN), available N (AN) and available Zn (AZn); pH values decreased widely comparing with the data reported by the National Soil Survey, especially in southeast China; the areas with low soil P concentrations (<10mgkg−1) were mainly located in southwest China; cation exchange capacity (CEC), available K (AK) and available Si (ASi) had similar trends in spatial distributions with high concentrations in southeast China and low concentrations in southwest. Notably, paddy fields in southeast China were typically deficient in AK due to their concentrations, all of which are almost less than 100mgkg−1. Correlation analysis revealed that rice yield was significantly positively correlated with pH, CEC, AK and ASi, while negatively correlated with AN and AZn. Low pH, AK, CEC and ASi levels may be the most important constraints limiting rice productivity and fertilizers with N and Zn may be over-used and must be applied appropriately. The soil quality index (SQI) was also calculated using the analyzed chemical properties and a significant correlation was observed between SQI and rice yield, supporting earlier findings that good soil chemical health is essential for optimum sustained crop production. The remarkable variations of SQI and rice yield indicated that the potential for increasing rice productivity is a real prospect. Therefore, regional planning fertilization should pay more attention to the spatial variability of soil chemical properties to avoid economic losses and environmental pollution, and especially to the limited nutrients.

Interannual and spatial variability of maple syrup yield as related to climatic factors.

Sugar maple syrup production is an important economic activity for eastern Canada and the northeastern United States. Since annual variations in syrup yield have been related to climate, there are concerns about the impacts of climatic change on the industry in the upcoming decades. Although the temporal variability of syrup yield has been studied for specific sites on different time scales or for large regions, a model capable of accounting for both temporal and regional differences in yield is still lacking. In the present study, we studied the factors responsible for interregional and interannual variability in maple syrup yield over the 2001–2012 period, by combining the data from 8 Quebec regions (Canada) and 10 U.S. states. The resulting model explained 44.5% of the variability in yield. It includes the effect of climatic conditions that precede the sapflow season (variables from the previous growing season and winter), the effect of climatic conditions during the current sapflow season, and terms accounting for intercountry and temporal variability. Optimal conditions for maple syrup production appear to be spatially restricted by less favourable climate conditions occurring during the growing season in the north, and in the south, by the warmer winter and earlier spring conditions. This suggests that climate change may favor maple syrup production northwards, while southern regions are more likely to be negatively affected by adverse spring conditions.

Modeling Differential Growth in Switchgrass Cultivars Across the Central and Southern Great Plains

Switchgrass (Panicum virgatum L.) has been recognized as a potential biofuel crop, because it is adapted to a wide range of environmental and climatic conditions. Zones of adaptation for many switchgrass cultivars are well documented and attributed to local adaptation to the temperature and photoperiod at the location of origin. The objective of this study is to develop cultivar-specific growth parameters for the Agricultural Land Management and Numerical Assessment Criteria (ALMANAC) model based on location of origin and use these parameters to predict the biomass production of two lowland cultivars (Alamo and Kanlow) and two upland cultivars (Blackwell and Cave-in-Rock) in the central and southern Great Plains (TX, AR, LA, OK, KS, and MO). The plant parameters adjusted for each cultivar’s origin include average growing season temperature (22–27 °C), photoperiod at growth onset (11.46–13.12 h), maximum number of heat units (1,500–2,300), maximum leaf area index (6–12), and light extinction coefficient (0.33–0.50). The absolute difference between the average simulated and measured yields across all seven field locations for each cultivar is less than 0.5 Mg ha−1. Performance of the cultivar-specific parameters varies by location, but the parameters do a reasonable job of estimating the average yield (less than 15 % difference) of each cultivar for a majority of field locations. In addition, regional simulations of the four cultivars each show realistic spatial variation in yield across the central and southern Great Plains. The parameters derived in this project for the ALMANAC model provide a tool for optimizing choice of switchgrass cultivar on different soils, in different climates, and with different management across large geographic regions.

Managing Spatial and Temporal Switchgrass Biomass Yield Variability

Biorefineries that plan to use switchgrass exclusively will encounter year-to-year variability in feedstock production. The economic success of the biorefinery will depend in part on the ability of the management team to strategically identify land for conversion from current use to the production of switchgrass enabling a flow of feedstock for the life of the biorefinery. The objective of this research is to determine the optimal quality, quantity, and location of land to lease while considering the spatial and temporal variability of switchgrass biomass yield. A calibrated biophysical simulation model was used to simulate switchgrass biomass yields for 50 years based on historical weather data from 1962 to 2011, for three land capability classes for each of 30 counties. Mathematical programming models were constructed and solved to determine the optimal leasing scheme for each of three strategies for a biorefinery that requires 2,000 Mg/day. As expected, a model based on the assumption that the average yield would be obtained in each year finds that production from land identified for leasing would be insufficient to fulfill the biorefinery’s needs in half of the years. In the absence of other sources of biomass, the feedstock shortage would require forced idling of the biorefinery for an average of 29.5 days during these years. Results of a strategy of leasing sufficient land to cover feedstock needs in the worst year from among 50 years for which data are available are compared to that of a strategy enabling year-to-year storage.

Evaluation of three simulation approaches for assessing yield of rainfed sunflower in a Mediterranean environment for climate change impact modelling

The determination of the impact of climate change on crop yield at a regional scale requires the development of new modelling methodologies able to generate accurate yield estimates with reduced available data. In this study, different simulation approaches for assessing yield have been evaluated. In addition to two well-known models (AquaCrop and Stewart function), a methodological proposal considering a simplified approach using an empirical model (SOM) has been included in the analysis. This empirical model was calibrated using rainfed sunflower experimental field data from three sites located in Andalusia, southern Spain, and validated using two additional locations, providing very satisfactory results compared with the other models with higher data requirements. Thus, only requiring weather data (accumulated rainfall from the beginning of the season fixed on September 1st, and maximum temperature during flowering) the approach accurately described the temporal and spatial yield variability observed (RMSE = 391 kg ha−1). The satisfactory results for assessing yield of sunflower under semi-arid conditions obtained in this study demonstrate the utility of empirical approaches with few data requirements, providing an excellent decision tool for climate change impact analyses at a regional scale, where available data is very limited.

Linking Temporal and Spatial Variability of Millennial and Decadal-Scale Sediment Yield to Aquatic Habitat in the Columbia River Basin

Linking Temporal and Spatial Variability of Millennial and Decadal-Scale Sediment Yield to Aquatic Habitat in the Columbia River Basin

Assessment of regional variability in crop yields with spatial autocorrelation: Banana farms and policy implications in Martinique

Agricultural research can support farmers and policy makers’ decisions by identifying the causes of spatial variability in crop yield at a regional level. In this paper, we propose a method that combines spatial autocorrelation measures and a farm network survey. This method is intended to describe the causes of spatial variability in crop yields, along with key crop management practices for reaching the best yields and the physical and socio-economic constraints of adopting these practices. This causal and hierarchical analysis of cropping system performance has the advantage of (1) preventing bias in the correlation between variables from the yield gap analysis and (2) formulating spatially targeted policies that are aimed at relaxing adoption constraints at the territorial level. After introducing the method and its different steps, we present the results of the assessment of the spatial variability in banana yields in Martinique (Caribbean). Our study has clearly shown that the planting stage is one of the most important aspects of banana production: allowing a long fallow period, plowing for soil preparation and using seedlings that are produced by tissue culture were associated with the best yields. However, several constraints limit their adoption by farmers at the regional level. The limiting factors were steep slopes, small farm size and low cash flow. We observed no relationship between pesticide use and yields. These study results finally permit the elaboration of spatially targeted policy recommendations to improve crop yields in a sustainable manner. It mainly consists in promoting and facilitating the adoption of good plantation practices for smallholders.

Using Nonlinear Geostatistical Models in Estimating the Impact of Salinity on Crop Yield Variability

Three nonlinear geostatistical models—disjunctive kriging (DK), indicator kriging (IK), and probability kriging (PK)—were used to develop conditional probability (CP) maps based on soil salinity thresholds for two crops, alfalfa (Medicago sativa L.) and corn (Zea mays L.). The CP maps divide alfalfa and corn fields into zones with different probabilities to reach a specific yield potential (YP) at a specific soil salinity threshold. The objectives of this study were to: (i) compare the performance of the DK, IK, and PK models in developing CP maps; (ii) compare actual alfalfa and corn yield samples with the YP estimated by the three models; and (iii) provide guidance for precision management of agriculture by considering the output of the models used in this study. Yield data were collected at alfalfa and corn fields to compare the actual data with those estimated by the models. The results of this study show that the CP maps developed using the three geostatistical models are efficient in assessing the impact of soil salinity on the spatial variability of alfalfa and corn yield. The comparison of the actual yield data with the estimated CP maps from the three models showed good agreement. The IK and PK models generated very similar estimates for each of the zones; however, the zones generated by both of these models are slightly different from the zones generated using the DK model. The information from this study can be used for precision management of agricultural resources.

Spatial Variability of Corn Yield and Yield Response to Fertilizer Nitrogen: Role of Organic Carbon

Utility of organic carbon (OC) content as an indicator of corn (Zea maize L.) grain yield and yield response to nitrogen (N) across a broad range of conditions was assessed using data from two studies (the first on a variable landscape at Elora Research Station and the second on 19 landscapes in private farms) conducted from 1998 to 2003 in southern Ontario. Zero and 140/150 kg N ha−1 were applied in all studies. Yields generally increased linearly or quadratically with increasing OC. Regression models indicated 42% of the data sets had no response to fertilizer N and in only two data sets in Elora and no data sets in private farms were yield responses to N dependent on OC. When yield increased with fertilizer addition, it was similar for all OC contents in a landscape. We concluded that OC content cannot be used to identify N-management units in southern Ontario.

Modeling Crop Yield in West‐African Rainfed Agriculture Using Global and Local Spatial Regression

Performance of crop yield models is generally evaluated without testing their ability to capture yield spatial variability across a large area. Local soil and environmental conditions or management factors usually cause significant crop yield variability. In West Africa, landscape heterogeneity and data scarcity pose yet additional challenges to crop yield modeling. In this study, conditional autoregression (CAR) and geographical weighted regression (GWR) were used to better understand spatial patterns in sorghum [Sorghum bicolor (L.) Moench.], pearl millet [Pennisetum glaucum (L.) R. Br.], and cotton (Gossypium hirsutum L.) yields in Burkina Faso. A series of SPOT satellite normalized difference vegetation index (NDVI) 1‐km2 10‐d composite images spanning the crop growing season and observations of rainfall, topography, soil properties, and labor availability were used as explanatory variables in the CAR and GWR models. Regression analyses revealed that crop yield was significantly related to rainfall and topography in the semiarid and subhumid agroecological zones of Burkina Faso. Soil properties and labor availability mainly affected sorghum and millet yields in the semiarid zone. By addressing spatial dependency between crop yield observations in the two zones, GWR outperformed CAR models. For CAR models, adjusted R2 (Ra2) values for the sorghum, millet, and cotton yields were 0.76, 0.70, and 0.50, respectively, for the semiarid zone and 0.54, 0.32, and 0.30, respectively, for the subhumid zone. For GWR models, Ra2 values were 0.85, 0.70, 0.78, respectively, for the semiarid zone and 0.76, 0.67, 0.65, respectively, for the subhumid zone. Thus, despite limited data availability, GWR can be used to model the spatial variability of crop yields across large areas in West Africa.

Significant yield benefits from honeybee pollination of faba bean (Vicia faba) assessed at field scale

There is growing evidence globally that improved pollination practices can help support higher yield and reduced variability for a wide range of insect pollinated crops. Managed honeybees are provided to pollinate some orchard crops, but they are less commonly used for field crops, in part because of uncertainty as to whether the potential benefits are justified by the costs. This uncertainty comes from a lack of studies conducted at the appropriate scale. In this study we examine the yield benefits from managed honeybees applied to Vicia faba in field scale trials. We provided honeybee hives to 17 fields in South Australia, and observed that bee activity and fruits per stem decreased with increasing distance from hives. We examined the spatial pattern of yield using yield map data collected at harvest and found there was an effect of distance from hives on mean yield (declining with distance) and spatial variability of yield (increasing with distance). The presence of a distance gradient was consistent across all fields, across two years, two V. faba varieties and two different bee-hive management methods. The average benefit is estimated to be an additional 17% yield, 90% of which is attained within 767m of hives. Our economic analysis indicates that provision of hives is profitable for a wide range of realistic values for crop value (dollars per tonne) and pollination cost (dollars per hive).

Predicting yields of short‐rotation hybrid poplar (Populus spp.) for the United States through model–data synthesis

Hybrid poplar (Populus spp.) is an important biomass crop being evaluated for cellulosic ethanol production. Predictions of poplar growth, rotation period, and soil carbon sequestration under various growing conditions, soils, and climates are critical for farmers and managers planning to establish short-rotation forestry (SRF) plantations. In this study, we used an ecoinformatics workflow, the Predictive Ecosystem Analyzer (PEcAn), to integrate literature data and field measurements into the Ecosystem Demography 2 (ED2) model to estimate yield potential of poplar plantations. Within PEcAn 164 records of seven different traits from the literature were assimilated using a Bayesian meta-analysis. Next, variance decomposition identified seven variables for further constraint that contributed > 80% to the uncertainty in modeled yields: growth respiration, dark respiration, quantum efficiency, mortality coefficient, water conductance, fine-root allocation, and root turnover rate. Assimilation of observed yields further constrained uncertainty in model parameters (especially dark respiration and root turnover rate) and biomass estimates. Additional measurements of growth respiration, mortality, water conductance, and quantum efficiency would provide the most efficient path toward further constraint of modeled yields. Modeled validation demonstrated that ED2 successfully captured the interannual and spatial variability of poplar yield observed at nine independent sites. Site-level analyses were conducted to estimate the effect of land use change to SRF poplar on soil C sequestration compared to alternate land uses. These suggest that poplar plantations became a C sink within 18 years of conversion from corn production or existing forest. Finally, poplar yields were estimated for the contiguous United States at a half degree resolution in order to determine potential productivity, estimate the optimal rotation period, and compare poplar to perennial grass yields. This regional projection suggests that poplar yield varies considerably with differences in soil and climate, reaching as much as 18 Mg x ha(-1) x yr(-1) in eastern, southern, and northwest regions. In New England, the upper Midwest, and northern California, yields are predicted to exceed those of the highly productive C4 perennial grass, Miscanthus. In these poplar-productive regions, 4-11 year rotations give the highest potential yields. In conclusion, poplar plantations are predicted to have a high yield potential across a wide range of climates and soils and could be sustainable in soil C sequestration.

An application of PCA and fuzzy C-means to delineate management zones and variability analysis of soil

Within-field variability is a well-known phenomenon and its study is at the centre of precision agriculture (PA). In this paper, site-specific spatial variability (SSSV) of apparent Electrical Conductivity (ECa) and crop yield apart from pH, moisture, temperature and di-electric constant information was analyzed to construct spatial distribution maps. Principal component analysis (PCA) and fuzzy c-means (FCM) clustering algorithm were then performed to delineate management zones (MZs). Various performance indices such as Normalized Classification Entropy (NCE) and Fuzzy Performance Index (FPI) were calculated to determine the clustering performance. The geo-referenced sensor data was analyzed for within-field classification. Results revealed that the variables could be aggregated into MZs that characterize spatial variability in soil chemical properties and crop productivity. The resulting classified MZs showed favorable agreement between ECa and crop yield variability pattern. This enables reduction in number of soil analysis needed to create application maps for certain cultivation operations.

Avaliação do ARCMUSLE para estimativa da produção de sedimentos na bacia hidrográfica do alto rio Negro, região sul brasileira

Water erosion is one of the main processes which cause soil degradation in tropical environments. This process occurs by the interactions between hydrological, geomorphological and pedological factors that cause the detachment and transport of particles. Through process understanding it is possible to make use of mathematical models in order to estimate the sediment yield in watersheds. The main objective of this paper is to apply the Modified Universal Soil Loss Equations (MUSLE) to simulate a rainfall event and evaluate the fit of this simulation to the measured data of sediment transport. MUSLE is a deterministic, empiric and originally lumped model that replaces the rainfall erosivity factor by the overland flow factor to simulate sediment yield in individual rainfall events. In addition to overland flow effects, the model considers the soil erodibility factor (K), hillslope length and steepness (LS), crop and management factor (P) and the erosion control practice factor (C) to perform simulations. The ArcMUSLE model was used integrated with a Geographical Information System (GIS) allowing an estimate, entirely for isolated events, of the overland flow and the sediment yield through a spatially distributed form. The model was applied to the Upper Negro River watershed, located between the geographic coordinates 25°55’S and 26°14’S, 48°56’W and 49°23’W, and drains an area of 788km². For the simulated event, with total rainfall of 60mm, results showed that both, overland flow and sediment transport, were overestimated by the model. Although the overland flow and the sediment yield simulated were, respectively, 8,81% and 30,02% higher than measured data, the results allowed analysis of the spatial variability of sediment yield and to locate sediment source areas in the watershed.

Underlying causes of yield spatial variability and potential for precision management in rice systems

Our current understanding of the mechanisms driving spatiotemporal yield variability in rice systems is insufficient for effective management at the sub-field scale. The overall objective of this study was to evaluate the potential of precision management for rice production. The spatiotemporal properties of multiyear yield monitor data from four rice fields, representing varying soil types and locations within the primary rice growing region in California, were quantified and characterized. The role of water management, land-leveling, and the spatial distribution of soil properties in driving yield heterogeneity was explored. Mean yield and coefficient of variation at the sampling points within each field ranged from 9.2 to 12.1 Mg ha−1 and from 7.1 to 14.5 %, respectively. Using a k-means clustering and randomization method, temporally stable yield patterns were identified in three of the four fields. Redistribution of dissolved organic carbon, nitrogen, potassium and salts by lateral flood water movement was observed across all fields, but was only related to yield variability via exacerbating areas with high soil salinity. The effects of cold water temperature and land-leveling on yield variability were not observed. Soil electrical conductivity and/or plant available phosphorus were identified as the underlying causes of the within-field yield patterns using classification and regression trees. Our results demonstrate that while the high temporal yield variability in some rice fields does not permit precision management, in other fields exhibiting stable yield patterns with identifiable causes, precision management and modified water management may improve the profitability and resource-use efficiency of rice production systems.

Remote Sensing Based Yield Estimation in a Stochastic Framework — Case Study of Durum Wheat in Tunisia

Multitemporal optical remote sensing constitutes a useful, cost efficient method for crop status monitoring over large areas. Modelers interested in yield monitoring can rely on past and recent observations of crop reflectance to estimate aboveground biomass and infer the likely yield. Therefore, in a framework constrained by information availability, remote sensing data to yield conversion parameters are to be estimated. Statistical models are suitable for this purpose, given their ability to deal with statistical errors. This paper explores the performance in yield estimation of various remote sensing indicators based on varying degrees of bio-physical insight, in interaction with statistical methods (linear regressions) that rely on different hypotheses. Performances in estimating the temporal and spatial variability of yield, and implications of data scarcity in both dimensions are investigated. Jackknifed results (leave one year out) are presented for the case of wheat yield regional estimation in Tunisia using the SPOT-VEGETATION instrument. Best performances, up to 0.8 of R2, are achieved using the most physiologically sound remote sensing indicator, in conjunction with statistical specifications allowing for parsimonious spatial adjustment of the parameters.

Spatio-tempora variation in cassava (Manihot sp.) in forest savannah eco-climatic zone of Nigeria

Multi-locational trials were conducted in four selected locations with the forest-savannah agro-ecological zone of Nigeria to investigate the spatial variability in the yield of cassava. In an attempt to compare the yield of cassava within the forest-savannah eco-climatic zone of Nigeria, four locations including Ibadan, Abeokuta, Akure and Oshogbo were selected for field trials. Four improved cassava cultivation (TMS 30572, TMS 91934, TMS 50395 and TMS 30555) were grown between 2009 and 2010 in these locations and harvested at 12 months after planting. Results obtained showed that there were significant differences in the yield of cassava in the four selected locations with Ibadan having the highest tuberous root per hectare among the four locations. TMS 30572 had the highest (52,964 t. ha-1) number of tuberous root per hectare among the four varieties of cassava planted. This study revealed that cassava yield decreased with increase in rainfall distribution in the forest-savannah agro-ecological zone of Nigeria. Key words: Cassava, tuberous root, spartial variability, rainfall distribution.

Combining explanatory crop models with geospatial data for regional analyses of crop yield using field-scale modeling units

Crop models are used to predict yield and resource requirements as well as to evaluate different climate or management scenarios at a specific site. However, problems involving land use or global climate change encompass larger, more diverse, spatial scales and would benefit from simulating over broader areas using high-resolution, spatially-distributed data. A geospatial interface was developed to combine the explanatory potato crop model SPUDSIM with the geographic information system (GIS) software ArcGIS using the scripting language Python. Multiple geospatial input data layers were incorporated, including weather, soil, management, and land use. Modeling units (MUs) were defined as homogeneous field-scale areas created by the intersection of the input layers. Crop production was simulated for each unique combination of climate, soil, and management for MUs classified as cropland. The outputs (crop yield, water use, and nitrogen uptake) were mapped to show the spatial distribution within each county and aggregated to the county-level over the region of interest. An example was provided for potato production in Maine and illustrates how potential crop yield varies spatially over the state. The geospatial crop model showed evidence of both spatial and temporal variability of crop yield at the county level. The interface was designed to be flexible and easy to apply to applications such as evaluating crop production capacity and response under different scenarios.