Irrigated Maize Fields Research Articles

A dynamic surface conductance to predict crop water use from partial to full canopy cover

The Penman–Monteith (P–M) equation has been widely used to predict crop water use or evapotranspiration (ET) due to its simplicity and biophysically robust framework. Surface conductance (Gs), a key variable reflecting crop physiological and soil physical responses to changing environment, often is a significant impediment to the practical application of the P–M equation. Here, we derived a dynamic biophysical model of Gs after incorporating the combined contributions of crop canopy and soil based on: (a) dynamic fraction of canopy cover; (b) response of stomata to radiation intercepted by crop canopy, vapor pressure deficit, and soil water availability in the root zone; and (c) soil evaporation coefficient affected by radiation reaching soil surface and soil moisture. The dynamic Gs model with the P–M equation can predict the variation of Gs and ET from partial to full canopy cover as crop growing. The model was parameterized by measurements using the eddy covariance technique over an irrigated maize field in 2009, and validated using independent data in 2010. We found good data-model agreements between ET predicted by the dynamic Gs model with P–M equation and measurements for both half-hourly and daily time-scales from partial to full canopy cover. The model also produced satisfactory estimation for soil evaporation. Therefore, the model is an alternative approach to predict ET using P–M equation for partial to full crop canopy cover.

Variations of crop coefficient and its influencing factors in an arid advective cropland of northwest China

Understanding the variation and magnitude of crop coefficient (Kc) is important for accurate determination of crop evapotranspiration and water use. In this study, we calculated Kc in an irrigated maize field with ground mulching by eddy covariance evapotranspiration measurements during the whole growing periods in 2009 and 2010 in an arid region of northwest China. A semi-empirical practical approach for estimating Kc was proposed by introducing the dynamic fraction of canopy cover and incorporating the effect of leaf senescence as a function of days after sowing. The contribution of arid advection of sensible heat resulting from irrigation to Kc and the response of Kc to canopy conductance (Gc) were investigated. The averaged values of daily Kc were lower than typical values obtained previously without mulching due to decreasing effect of mulching on Kc, with 0.82 and 0.80 for the 2 years, respectively. The maximum average Kc occurred at the heading stage, with 1.21 and 1.04 for the 2 years, respectively. The difference of Kc was attributed to the difference of leaf area index. The semi-empirical practical approach could well estimate the variations of Kc, thus could be a robust and useful tool for the practical users and water managers. The contributions to daily Kc from the arid advection were 4.4–28.0% of the measured Kc. The Gc had stronger control on daily Kc at the early and later stages than at the middle stage. When Gc, leaf area index and relative soil extractable water were lower than the respective threshold values of 20 mm s−1, 3.0 m2 m−2 and 0.5, the daily Kc increased significantly with the increase of the three factors, and almost remained constant when the three factors were beyond the threshold values. These results are helpful for quantifying contributions of individual factors to Kc, and subsequently improving water management practices according to Kc. Copyright © 2014 John Wiley & Sons, Ltd.

Multiscale spectral analysis of temporal variability in evapotranspiration over irrigated cropland in an arid region

The temporal patterns of evapotranspiration (ET) and its biophysical and physiological controls (e.g., soil water content, solar radiation, and canopy conductance) occur over a wide range of time-scales ranging from seconds to decades. Thus, there is increasing interest in understanding at which scales the main temporal correlations between ET and its controlling factors occur across different ecosystems. For this study, we used eddy covariance measurements of ET over 2 years at an irrigated maize field in an arid inland region of northwest China. We applied the wavelet transform as a novel technique to examine spectral characteristics of ET and its controlling factors. The ET power spectra displayed a −1 power law in turbulent inertial subrange at <1-h time-scale, and showed substantial power at daily, seasonal and annual time-scales. The cospectra of ET and soil water content (SWC) showed significant temporal correlation at 5-days, which has implications for calculation of ET using the soil water balance method in this region. We found that ET synchronized with the change of net radiation, and led vapor pressure deficit and air temperature for ∼2h at the 1-day time-scale (i.e., positive lags), but the phase relationship between ET and SWC was influenced by irrigation patterns. Canopy conductance influenced ET variability at the 1-day time-scale, but the effect was not consistent across the growing seasons. Our results show the importance of irrigation practices and its influence on the multi-temporal correlations of ET and its controlling factors. Irrigation can sharply change the phase angle relationship of ET and SWC from −10h to 10h. These results are important for understanding water cycle processes, improving water management, and address food security issues across irrigated croplands in arid regions.

Partitioning evapotranspiration into soil evaporation and transpiration using a modified dual crop coefficient model in irrigated maize field with ground-mulching

The accurate partitioning of crop evapotranspiration (ETc) into two components, soil evaporation (Es) and transpiration (Tr), is needed to better understand terrestrial hydrological cycles and develop precise irrigation scheduling. However, there is no easy way to distinguish between the two. Based on FAO-56 dual crop coefficient (Kc) approach, we developed a modified dual Kc model for better predicting Tr through basal crop coefficient (Kcb) and Es through evaporation coefficient (Ke). Daily Kcb was dynamically calculated by introducing a canopy cover coefficient that could be simply described as a function of leaf area index or fraction of canopy cover. Also, leaf senescence factor was taken into consideration to modify Kcb when leaf suffers functional senescence. Ke was modified through introducing the fraction of ground-mulching (fm) to account for the effect of mulching on Es. The model was parameterized by measurements in 2009, and validated using independent data for grain and seed maize with and without mulching in 2010 and 2011. The results indicate that the predicted Kc values by the modified model were obviously better than those by the original model. The good agreements were found between the predicted ETc, Tr and Es using the modified model and the measurements for grain maize in 2010 with fm=0.6, with the slope of linear regression of 0.99 (R2=0.90), 1.01 (R2=0.92) and 0.96 (R2=0.78), respectively. The modified model also well reproduced the values of ETc and Es for seed maize in 2011, which had lower plant height and leaf area index compared to grain maize, under mulching (fm=0.7) and non-mulching (fm=0) conditions. The slopes of linear regression between predictions and measurements were 0.98 (R2=0.91) and 0.99 (R2=0.92) for ETc, and 0.98 (R2=0.79) and 0.97 (R2=0.80) for Es under fm=0.7 and 0.0, respectively. These results suggest that the modified dual Kc model can accurately predict ETc, Es and Tr for different crop types under different mulching, thus could be a useful tool for improving irrigation water management.

Study of crop coefficient and the ratio of soil evaporation to evapotranspiration in an irrigated maize field in an arid area of Yellow River Basin in China

A field experiment was conducted in a maize field in 2006 in an arid area of the Yellow River Basin in China. The daytime evapotranspiration (ETc) and soil evaporation beneath the maize canopy (Eg) were measured by Bowen ratio energy balance method and micro-lysimeters, respectively. The results showed that the total ETc during maize growth season was 696 mm, and the maximum values occurred at about 90–140 days after sowing. The crop coefficient (Kc), which was calculated from the ratio of ETc to reference evapotranspiration (ET0), was quite different from the values reported by other researchers in similar climate areas, with average values of 0.34, 0.47, 1.0 and 0.9 for initial, development, mid-season and late-season stages, respectively. High correlations between leaf area index (LAI) and average Kc for every 4 days were obtained. The total Eg was 201.4 mm with average values ranged from 0.92 to 2.05 for four growth stages of maize; and accounted for around 28.9 % of ETc. The ratio Eg/ETc showed high negative relationship with LAI. These results were very important in precise management of irrigation for maize in Yellow River Basin areas.

Combination of ground and satellite data for the operational estimation of daily evapotranspiration

A recent paper of our research group has proposed a simplified “water balance” model which predicts actual evapotranspiration (ETA) based on ground and remotely sensed data. The model combines estimates of potential evapotranspiration (ET0) and of fractional vegetation cover derived from NDVI in order to separately simulate transpirative and evaporative processes. The new method, named NDVI-Cws, was validated against latent heat measurements taken by the eddy covariance technique over various vegetation types in Central Italy. The current paper extends this validation to three other test sites in Tuscany for which reference data are obtained from different sources. In the first two sites (non-irrigated winter wheat and irrigated maize fields) seasonal reference ETA data series are obtained by the WinEtro model. In situ transpiration measurements are instead used as reference data for a deciduous oak forest stand. The ETA and transpiration estimates of the NDVI-Cws method are very similar to the reference data in terms of both annual totals and seasonal evolutions. Examples are finally provided of the model application for operationally monitoring ETA in Tuscany.

Maize and Soybean Litter‐Carbon Pool Dynamics in Three No‐Till Systems

After harvest, the litter‐C pool contributes 20 to 23% of the total C present in maize (Zea mays L.)‐based agricultural ecosystems. Therefore, understanding litter‐C pool dynamics is important in determining the overall C dynamics of the system and its potential to sequester C. We examined litter‐C production and in situ decomposition of maize and soybean [Glycine max (L.) Merr.] litter using four annual litter cohorts (2001–2004) in three no‐till management regimes: irrigated continuous maize, irrigated maize–soybean rotation, and rainfed maize–soybean rotation. Litter inputs, i.e., litter‐C production, was 20 to 30% higher in irrigated fields than the rainfed field, and maize produced approximately twice as much litter C as soybean. Litter losses, i.e., decomposition, were highly variable, but overall, after 3 yr of decomposition, only 20% litter C remained on average. We fit decomposition models to our data to predict litter‐C accretion after 10 yr of management. While management and annual variation were important in fitting the model, tissue type increased model fit most, suggesting a strong role of litter physical structure in decomposition. The predicted 10‐yr standing litter pool was 15 and 35% higher in the irrigated maize field than the irrigated or rainfed maize–soybean rotations, respectively. Our data clearly show that the litter‐C pool is highly dynamic, with as much as a 60% increase within 1 yr. Thus, short‐term C sequestration estimates in agricultural ecosystems largely reflect litter‐C pool changes, which are primarily driven by litter inputs and not decomposition differences.

Soil water status and water table depth modelling using electromagnetic surveys for precision irrigation scheduling

Electromagnetic surveys have been used to quantify soil variability with respect to soil water storage in an irrigated maize field. A fluctuating water table sub-irrigates the crop in some places, and a wireless sensor network simultaneously monitors real-time depth of water table and soil moisture content, with large differences in soil moisture measured at any one time in these uniformly textured sands. These large differences justify assessment of the spatio-temporal variability of soil hydraulic properties when aiming for precision management of the resource. Regression models were used to spatially predict water table depth and moisture content at 50cm using EM38 survey data, a rainfall time series and a wetness index extracted from a digital elevation model. A multiple linear regression modelling (MLM) approach was compared with a data‐mining approach using a random forest model (RF). The RF model implements a more thorough interrogation of the data using classification trees with subsequent regression of the data and provided the best prediction of soil moisture (R2=0.94; RMSE=0.03m3m−3 using RF; R2=0.77; RMSE=0.06m3m−3 using MLM) and water table depth (R2=0.91; RMSE=7.17cm using RF; R2=0.71; RMSE=12.48cm using MLM).

Evapotranspiration measurement and estimation using modified Priestley–Taylor model in an irrigated maize field with mulching

Accurate measurement or estimation of crop evapotranspiration (ET) is important to develop exact irrigation scheduling and reasonably use water resources. ET of an irrigated maize field mulched with plastic film was measured using eddy covariance technique over two growing seasons in an arid region of northwest China. A modified Priestley–Taylor (PT) model was developed, incorporating the effect of leaf area, soil moisture, mulching fraction and leaf senescence on ET. The model was parameterized by field measurements in 2008 and validated by those in 2009. Results indicate that diurnal variation of ET was bell-shaped curve for all the growing stages. During the two growing seasons, total ET was 503.1 and 562.4mm, and mean daily ET 3.47 and 3.54mmd−1, respectively. ET was mainly controlled by solar radiation, and significantly affected by influential factors below the thresholds, which were leaf area index of 3.0m2m−2, and soil relative extractable water of 0.5, and canopy conductance of 20mms−1, respectively. A good agreement was found between ET estimated by the modified PT model with observations, with linear slope of 0.99 and R2 of 0.94 and 0.96 for half-hourly and daily time scale, respectively. Thus the modified PT model can be used to estimate ET or quantify the effect of controlling factors on ET in similar agricultural fields.

Environmental and economic evaluation of N fertilizer rates in a maize crop in Italy: A spatial and temporal analysis using crop models

Crop simulation models have the potential to assess the risk associated with the selection of a specific N fertilizer rate, by integrating the effects of soil-crop interactions on crop growth under different pedo-climatic and management conditions. The objective of this study was to simulate the environmental and economic impact (nitrate leaching and N 2 O emissions) of a spatially variable N fertilizer application in an irrigated maize field in Italy. The validated SALUS model was run with 5 nitrogen rates scenarios, 50, 100, 150, 200, and 250 kg N ha −1 , with the latter being the N fertilization adopted by the farmer. The long-term (25 years) simulations were performed on two previously identified spatially and temporally stable zones, a high yielding and low yielding zone. The simulation results showed that N fertilizer rate can be reduced without affecting yield and net return. The marginal net return was on average higher for the high yield zone, with values ranging from 1550 to 2650 € ha −1 for the 200 N and 1485 to 2875 € ha −1 for the 250 N. N leaching varied between 16.4 and 19.3 kg N ha −1 for the 200 N and the 250 N in the high yield zone. In the low yield zone, the 250 N had a significantly higher N leaching. N 2 O emissions varied between 0.28 kg N 2 O ha −1 for the 50 kg N ha −1 rate to a maximum of 1.41 kg N 2 O ha −1 for the 250 kg N ha −1 rate. ► The best amount of N fertilizer to apply on a field varies spatially and temporally. ► A crop simulation model was used to select optimal N rate over space and time. ► Net marginal return increased using the N rate chosen by model. ► N leaching and N 2 O emission were reduced with the N rate selected by the model.

Large-Scale On-Farm Implementation of Soil Moisture-Based Irrigation Management Strategies for Increasing Maize Water Productivity

Irrigated maize is produced on about 3.5 Mha in the U.S. Great Plains and western Corn Belt. Most irrigation water comes from groundwater. Persistent drought and increased competition for water resources threaten long-term viability of groundwater resources, which motivated our research to develop strategies to increase water productivity without noticeable reduction in maize yield. Results from previous research at the University of Nebraska-Lincoln (UNL) experiment stations in 2005 and 2006 found that it was possible to substantially reduce irrigation amounts and increase irrigation water use efficiency (IWUE) and crop water use efficiency (CWUE) (or crop water productivity) with little or no reduction in yield using an irrigation regime that applies less water during growth stages that are less sensitive to water stress. Our hypothesis was that a soil moisture-based irrigation management approach in research fields would give similar results in large production-scale, center-pivot irrigated fields in Nebraska. To test this hypothesis, IWUE, CWUE, and grain yields were compared in extensive on-farm research located at eight locations over two years (16 site-years), representing more than 600 ha of irrigated maize area. In each site-year, two contiguous center-pivot irrigated maize fields with similar topography, soil properties, and crop management practices received different irrigation regimes: one was managed by UNL researchers, and the other was managed by the farmer at each site. Irrigation management in farmer-managed fields relied on the farmers’ traditional visual observations and personal expertise, whereas irrigation timing in the UNL-managed fields was based on pre-determined soil water depletion thresholds measured using soil moisture sensors, as well as crop phenology predicted by a crop simulation model using a combination of real-time (in-season) and historical weather data. The soil moisture-based irrigation regime resulted in greater soil water depletion, which decreased irrigation requirements and enabled more timely irrigation management in the UNL-managed fields in both years (34% and 32% less irrigation application compared with farmer-managed fields in 2007 and 2008, respectively). The average actual crop evapotranspiration (ETC) for the UNL- and farmer-managed fields for all sites in 2007 was 487 and 504 mm, respectively. In 2008, the average UNL and average farmer-managed field had seasonal ETC of 511 and 548 mm, respectively. Thus, when the average of all sites is considered, the UNL-managed fields had 3% and 7% less ETC than the farmer-managed fields in 2007 and 2008, respectively, although the percentage was much higher for some of the farmer-managed fields. In both years, differences in grain yield between the UNL and farmer-managed fields were not statistically significant (p = 0.75). On-farm implementation of irrigation management strategies resulted in a 38% and 30% increase in IWUE in the UNL-managed fields in 2007 and 2008, respectively. On average, the CWUE value for the UNL-managed fields was 4% higher than those in the farmer-managed fields in both years. Reduction in irrigation water withdrawal in UNL-managed fields resulted in $32.00 to $74.10 ha-1 in 2007 and $44.46 to $66.50 ha-1 in 2008 in energy saving and additional net return to the farm income. The results from this study can have significant positive implications in future irrigation management of irrigated maize systems in regions with similar soil and crop management practices.

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Energy consumption in production of grains prevalent in Saveh, Iran.

This paper examines the energy consumption of wheat, barley and maize production in Iran (Saveh area). This study was conducted over 28,400 ha of irrigated wheat, barley and maize fields and 19,300 ha of dry land wheat and barley fields in Saveh, a central city, Iran, in the harvest year of 2003 - 2004. The data was collected from three different sources: questionnaire, literature review and field measurement. Total energy consumption for irrigated wheat, barley and maize were estimated as 51587, 53529 and 72743 MJ/ha, respectively. Also energy consumption for wheat and barley in dry land system were estimated as 12543 and 11935 MJ/ha, respectively. The average operational energy consumption for cultivation of wheat (irrigated), wheat (dry land), barley (irrigated) , barley (dry land) and maize were 36107, 6067, 40498, 6250 and 48632 MJ/ha, respectively. The maximum energy consumed in these productions, for irrigated farming systems and dry land farming system were irrigation and tillage, respectively. Fuel consumption is one of the important input elements, which must be taken under close managerial supervision in order to optimize the amount of energy consumed.

Surface energy balance model of transpiration from variable canopy cover and evaporation from residue-covered or bare-soil systems

A surface energy balance model based on the Shuttleworth and Wallace (Q J R Meteorol Soc 111:839–855, 1985) and Choudhury and Monteith (Q J R Meteorol Soc 114:373–398, 1988) methods was developed to estimate evaporation from soil and crop residue, and transpiration from crop canopies. The model describes the energy balance and flux resistances for vegetated and residue-covered surfaces. The model estimates latent, sensible and soil heat fluxes to provide a method to partition evapotranspiration (ET) into soil/residue evaporation and plant transpiration. This facilitates estimates of the effect of residue on ET and consequently on water balance studies, and allows for simulation of ET during periods of crop dormancy. ET estimated with the model agreed favorably with eddy covariance flux measurements from an irrigated maize field and accurately simulated diurnal variations and hourly amounts of ET during periods with a range of crop canopy covers. For hourly estimations, the root mean square error was 41.4 W m−2, the mean absolute error was 29.9 W m−2, the Nash–Sutcliffe coefficient was 0.92 and the index of agreement was 0.97.

Ecosystem water use efficiency in an irrigated cropland in the North China Plain

The eddy covariance technique and the cuvette method were used to investigate water use efficiency in an irrigated winter wheat (Triticum asetivum L.)/summer maize (Zea mays L.) rotation system in the North China Plain. The results show that ecosystem water use efficiency (WUE(e)) changed diurnally and season, ally. Daily maximal WUE(e) appeared in the morning. WUE(e) generally peaked in late April in wheat field and in late July/early August in maize field. From 2003 to 2006, seasonal mean WUE(e) was 6.7-7.4 mg CO(2) g(-1) H(2)O for wheat and 8.4-12.1 mg CO(2) g(-1) H(2)O for maize. WUE(e) was much lower than canopy water use efficiency (WUE(c)) under small leaf area index (LAI) but very close to WUE(c) under large LAI. With the increase in LAI, WUE(e) enlarged rapidly under low LAI but slowly when LAI was higher than one. WUE(e) was greater on the cloudy days than on the sunny days. Under the same solar radiation, WUE(e) was higher in the morning than in the afternoon. The ratio of internal to ambient CO(2) partial pressure (C(i/)C(a)) decreased significantly with the increase in photosynthetically active radiation (PAR) when PAR was lower than the critical values (around 500 and 1000 mu mol m(-2) s(-1) for wheat and maize, respectively). Beyond critical PAR, C(i)/C(a) was approximately constant at 0.69 for wheat and 0.42 for maize Therefore., when LAI and solar radiation was large enough, WUE(e) has negative correlation with vapor pressure deficit in both of irrigated wheat and maize fields. (C) 2009 Elsevier B.V. All rights reserved.

Site‐Specific Nitrogen and Plant Density Management in Irrigated Maize

Economic or environmental benefits resulting from variable‐rate (VR) application of N and seed are uncertain in irrigated maize (Zea mays L.) systems with high yield potential. We evaluated different plant population and N management strategies in two irrigated maize fields in Nebraska. Inputs were varied according to yield zones derived from yield maps, detailed maps of soil organic matter (SOM), and sampled seasonal NO3 status. Uniform management following Best Management Practices (BMPs) resulted in high levels of grain yield (&gt;15 t ha−1 at Site 1; &gt;12 t ha−1 at Site 2), nitrogen use efficiency (NUE), and gross return above input cost. Management of high‐yielding irrigated systems on relatively flat terrain was not improved through the predictive site‐specific approaches tested in this study, which relied on available historical field information (yield maps, weather) and seasonal soil sampling. Among four site‐years, only one site‐year showed significant increases in yield and NUE and decrease in N input with the VR N. Yield interactions between VR N management strategies and plant population were not significant. More potential for increasing yields, resource efficiency, and profitability may exist through integrating such approaches with dynamic, in‐season management of water and N. Such approaches are emerging, but remain to be evaluated thoroughly, particularly under high‐yielding conditions and against conventional BMPs.

The yield pattern considering the distance to flow accumulation lines

Precision agriculture techniques imply a spatial management of fields and to do so a good understanding of the spatial and temporal variability of yield is needed. Average yield data from seven irrigated maize fields were used to study the yield pattern considering the distance of plants to flow accumulation lines. It was found that there is a significant correlation between average yield and distance to flow accumulation lines (DFL). This correlation is best represented by a polynomial function. The most common shape of the yield pattern curve considering the distance to flow accumulation lines shows that there is an increase in average yield with DFL from 0 to 12.5–17.5 m. Near the flow lines the average yield presents lower values due to drainage problems causing plant growth problems. It was also observed higher yield variability near the flow lines. For higher distances from the flow lines there is a continuous decrease in average yield due to less water availability and other variations of soil properties.

Active remote sensing and grain yield in irrigated maize

Advances in agricultural technology have led to the development of active remote sensing equipment that can potentially optimize N fertilizer inputs. The objective of this study was to evaluate a hand-held active remote sensing instrument to estimate yield potential in irrigated maize. This study was done over two consecutive years on two irrigated maize fields in eastern Colorado. At the six- to eight-leaf crop growth stage, the GreenSeeker™ active remote sensing unit was used to measure red and NIR reflectance of the crop canopy. Soil samples were taken before side-dressing from the plots at the time of sensing to determine nitrate concentration. Normalized difference vegetation index (NDVI) was calculated from the reflectance data and then divided by the number of days from planting to sensing, where growing degrees were greater than zero. An NDVI-ratio was calculated as the ratio of the reflectance of an area of interest to that of an N-rich portion of the field. Regression analysis was used to model grain yield. Grain yields ranged from 5 to 24 Mg ha−1. The coefficient of determination ranged from 0.10 to 0.76. The data for both fields in year 1 were modeled and cross-validated using data from both fields for year 2. The coefficient of determination of the best fitting model for year 1 was 0.54. The NDVI-ratio had a significant relationship with observed grain yield (r 2 = 0.65). This study shows that the GreenSeeker™ active sensor has the potential to estimate grain yield in irrigated maize; however, improvements need to be made.

Habitat of the Corn Leafhopper (Hemiptera: Cicadellidae) During the Dry (Winter) Season in Mexico

Although the corn leafhopper Dalbulus maidis (DeLong and Wolcott) is the most important vector of maize pathogens in Latin America, little is known about how and where it overwinters (passes the dry season), particularly in Mexico. The objectives of this study were (1) to monitor the abundance of D. maidis adults throughout the dry season in maize and maize-free habitats and (2) to determine where and how D. maidis adults, exposed or nonexposed to the maize pathogen Spiroplasma kunkelii Whitcomb, overwinter in a maize-free habitat. Work for the first objective was done during the two consecutive dry seasons of 1999-2000 and 2000-2001; the second objective was done during the dry seasons of 2003-2004 and 2005-2006. During the dry winter seasons, D. maidis was prevalent as long as maize was present in irrigated areas. The leafhopper was found in 52 of the 58 irrigated maize fields sampled in Mexico at the end of the dry seasons of 1999-2000 and 2000-2001. However, leafhopper adults were not found in nonirrigated maize-free habitats at high elevation during the dry winter season (February, March, and April), although leafhopper adults were prevalent on perennial wild grasses in January after maize harvest. Additional experiments revealed, however, that corn leafhopper adults, although few in number, survived the entire dry season in these nonirrigated maize-free fields. Also, no detectable difference in survival existed between leafhoppers exposed and those not exposed to S. kunkelli during the two dry seasons in the maize-free habitat.

Geostatistical simulation of the spatial variability of an irrigated maize farm plot

This paper proposes a methodology to spatialize the STICS crop model at the soil-unit scale in an agricultural field. The soil's apparent resistivity is used as auxiliary information in a geostatistical model of the soil depth. Stochastic simulations of some STICS input parameters showing spatial variability, as the soil depth, are used in order to spatialize this model. The methodology was tested on an irrigated maize field in Petite Beauce (France). Apparent resistivities at three different depths were determined automatically with MUlti-depth continuous electrical profiling (MUCEP) on a 1.4-ha area. Soil characteristics and irrigation were also measured on a grid to access to the field spatial variability of STICS inputs. Comparison of simulated yield and water contents at the end of harvesting with the field measurements is carried out for two different irrigation conditions, thanks to an irrigation restriction imposed on one subarea. The results show that the methodology is promising, but improvements are needed for practical uses.