Variation In Crop Growth Research Articles

A Study of the Relationships between Depths of Soil Constraints and Remote Sensing Data from Different Stages of the Growing Season

The presence of salinity and sodicity in the root zone can limit root development and impact crop yield. Topsoil constraints are likely to have the greatest impact on crop growth early in the growing season, when plant roots are still shallow. Later in the growing season, subsoil constraints may have a greater impact as roots reach deeper into the soil. This study investigated whether different patterns of spatial variation in crop growth would be evident in remote sensing data captured from different stages of the growing season, with the aim of providing a means of indicating whether soil constraints in the topsoil and in the subsoil might be impacting crop growth. If a topsoil constraint is impacting growth, we might expect its effects to show through a negative correlation between the soil constraint and the early-season vegetation index. However, we would not expect to observe the impact of a subsoil constraint until later in the season (when roots have reached the constraint). To test the results from the analysis of remote sensing data, we used soil data from five fields from across Australia’s northern grains-growing region. We used these data to assess soil constraint severity and correlations between the soil constraints and enhanced vegetation index (EVI). The results of the study were inconclusive, and it was difficult to identify a dominant soil constraint with a clear relationship to crop growth. The soil data were also insufficient to draw conclusions about the depths of any dominant soil constraints. Furthermore, there was a lot of subjectivity in the interpretations of the correlations between remote sensing and soil data. The study also investigated the consistency of the spatial variation in EVI over multiple years, but the results were still inconclusive. In conclusion, this study highlights the challenges of using remote sensing data to diagnose soil constraints in agricultural settings. While remote sensing can provide useful insights into crop growth, interpreting these data and drawing meaningful conclusions about soil constraints requires further research and development.

Towards improving the precision agriculture management of the wheat crop using remote sensing: A case study in Central Non-Black Earth region of Russia

Smart management of the wheat crop requires understanding the various variables affecting crop quality and quantity. The use of remote sensing data contributes to improve the application of precision agriculture (PA).The current paper aims to evaluate the efficiency of remote sensing in monitoring wheat growth and enhance the management practices. Three cultivation technologies for winter wheat were implemented: basic (CT1), intensive (CT2), and highly intensive (CT3) were investigated. Sentinel-2 with a resolution of 10 m was used to monitor the change in wheat growth under different management systems during 2019. The following variables: yield quantity, grain quality (measured protein and gluten), in addition, five vegetation indices: Soil Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI),Green Chlorophyll Index (GCI), Green Leaf Index (GLI) were retrieved during the growing season. The results showed that, GCI has the highest performance in predicting crop yield where r was 0.98. In addition, the SAVI and NDVI have the same performance; r was 0.96 for both protein and gluten contents. The yield production of CT3 has increased by 3 t/ha, in addition, the grain quality was superior compared to the CT1. The economic efficiency results showed that the CT3 was the most profitable for Moscovskaya 40 variety (WV1) with 2,72 Payback. For Nemchinovskaya 17 variety (WV2), the most profitable cultivation technology was the CT1 and CT2 with 2,44 Payback, and for the new variety Nemchinovskaya 85, the CT2 was the most profitable with the Payback of 3,03. Finally, remote sensing shows the spatial variation in crop growth, which enhances crop management to achieve optimal production in terms of quantity and quality.

Simulation of maize crop growth using an improved crop model considering the disintegrated area of biodegradable film

• An improved crop growth model (FCM) under biodegradable film mulching was developed. • FCM model performed well in capturing the temporal trends in crop growth dynamics. • Biodegradable film mulching results in a slight decrease of crop yield. • An irrigation depth of 22.5 mm as optimal treatment was recommended in arid region. • Disintegrated rate should not exceed 63 % and 38 % in 2018 and 2019, respectively. Biodegradable film is recognized as a promising alternative to plastic film within crop mulching. This is because biodegradable film disintegrates into water and carbon dioxide through soil microorganisms-facilitated catalytic decomposition during the crop growth season. However, biodegradable film mulching (BFM) also has some disadvantages, such as the water and heat conservation being poor during nighttime period. Crop growth and yield is reduced by a suboptimal distribution of soil water and temperature. Therefore, there is value in assessing the effects of BFM on crop growth during daytime and nighttime periods, and particularly under different irrigation levels. This study developed a crop growth model (FCM) considering the disintegrated area of biodegradable film mulching to capture crop growth dynamics under different soil water contents and mulching areas. The disintegrated area of biodegradable film was determined by an image method. The model was firstly calibrated and validated using observed data collected over two years (2018 and 2019) under three mulching treatments [plastic film mulching (PFM), biodegradable film mulching (BFM), and no film mulching (NFM)] and three irrigation levels (irrigation depths of 30, 22.5, and 15 mm). The model was then used to evaluate crop growth rate ( k ), crop yield ( CY ), and water use efficiency (WUE) during daytime and nighttime periods. The results showed that the simulation accuracy of the FCM model was sufficiently high under different treatments, with a root mean square error ( RMSE) of 284.6 kg ha –1 . The benefits of BFM for crop growth were similar to that of PFM under a low range of effective accumulated soil temperature (EAST). However, differences in crop growth variables between PFM and BFM increased with an increase in EAST. Average leaf area index ( LAI ) and aboveground dry biomass ( AB ) during both years increased by 16.9 % and 39.2 % under PFM compared to that under BFM, respectively. In general, the harvest index ( HI ), CY , and water use efficiency (WUE) under BFM were lower than that under PFM. There were comparable variations in crop growth under different irrigation levels. The highest WUE occurred in the BFM 22.5 treatment at 44.5 kg ha –1 mm –1 . Therefore, this study recommends an irrigation depth of 22.5 mm as an optimal irrigation treatment in an arid region.

Improving the representation of cropland sites in the Community Land Model (CLM) version 5.0

Abstract. The incorporation of a comprehensive crop module in land surface models offers the possibility to study the effect of agricultural land use and land management changes on the terrestrial water, energy, and biogeochemical cycles. It may help to improve the simulation of biogeophysical and biogeochemical processes on regional and global scales in the framework of climate and land use change. In this study, the performance of the crop module of the Community Land Model version 5 (CLM5) was evaluated at point scale with site-specific field data focusing on the simulation of seasonal and inter-annual variations in crop growth, planting and harvesting cycles, and crop yields, as well as water, energy, and carbon fluxes. In order to better represent agricultural sites, the model was modified by (1) implementing the winter wheat subroutines following Lu et al. (2017) in CLM5; (2) implementing plant-specific parameters for sugar beet, potatoes, and winter wheat, thereby adding the two crop functional types (CFTs) for sugar beet and potatoes to the list of actively managed crops in CLM5; and (3) introducing a cover-cropping subroutine that allows multiple crop types on the same column within 1 year. The latter modification allows the simulation of cropping during winter months before usual cash crop planting begins in spring, which is an agricultural management technique with a long history that is regaining popularity as it reduces erosion and improves soil health and carbon storage and is commonly used in the regions evaluated in this study. We compared simulation results with field data and found that both the new crop-specific parameterization and the winter wheat subroutines led to a significant simulation improvement in terms of energy fluxes (root-mean-square error, RMSE, reduction for latent and sensible heat by up to 57 % and 59 %, respectively), leaf area index (LAI), net ecosystem exchange, and crop yield (up to 87 % improvement in winter wheat yield prediction) compared with default model results. The cover-cropping subroutine yielded a substantial improvement in representation of field conditions after harvest of the main cash crop (winter season) in terms of LAI magnitudes, seasonal cycle of LAI, and latent heat flux (reduction of wintertime RMSE for latent heat flux by 42 %). Our modifications significantly improved model simulations and should therefore be applied in future studies with CLM5 to improve regional yield predictions and to better understand large-scale impacts of agricultural management on carbon, water, and energy fluxes.

Critical period for seed number determination in soybean as determined by crop growth rate, duration, and dry matter accumulation

• We used two independent approaches to identify the beginning and end of the critical period for seed number determination in soybean. • Critical period for seed number determination occurs during the R3-R6 phase. • Seed number was related to biomass accumulation during the critical period. • Accumulated biomass is a better predictor of seed number than crop growth rate or duration alone. Seed number (SN) is positively associated with aboveground dry matter (ADM) accumulation in most crop species. In soybean, there are discrepancies among studies relative to the start and end of the critical period for seed number determination. The objective of this study was to more precisely identify those two stages. To do so, we followed two approaches: (i) analyses of experimental data where differences in SN occurred as a result of variation in crop growth due to weather and management practices and (ii) review of published experiments where soybean was subjected to artificial stresses during specific crop phases. Following the first approach, we analyzed the relationships between SN versus ADM accumulation and its components, crop growth rate (CGR) and duration, for different reproductive phases using data from experiments that portrayed a wide range of environments and management practices. In the second approach, we compared SN values in stressed versus non-stressed treatments. Accumulated ADM during the R3 to R6 phase explained ca. 70 % of the variation in SN across experiments. The CGR during R3-R6 was also a good predictor of SN, but it had lower explanatory power compared with accumulated ADM, because CGR did not account for changes in duration of critical period across experiments. Review of published data, wherein soybean crops were subjected to artificial stresses during different reproductive phases, confirmed that the R3-R6 phase is a critical phase for SN determination in soybean. Key findings of this study were: (i) critical period for SN determination in soybean occurs during the R3-R6 phase and (ii) accumulated ADM during the critical period is a better predictor of SN compared with CGR because it accounts for differences in the duration of the critical period as a result of weather and management.

A modified SWAP model for soil water and heat dynamics and seed–maize growth under film mulching

Soil–Water–Atmosphere–Plant (SWAP) model fails to consider the impacts of plastic film mulching on soil moisture and heat regimes, and crop growth and yield. In this study, SWAP model was modified in the modules of precipitation interception, soil evaporation, soil temperature, and crop growth to accommodate the changes of soil moisture and soil temperature caused by film mulching and the consequent variation of crop growth. The modified SWAP model was compared with original SWAP model. Here, the original model showed the case where there was no film mulching effect under the same input conditions as the modified model. The models were calibrated and validated by a field experiment of seed–maize conducted in the Shiyang River Basin of Northwest China in 2017, 2018 and 2019. The experiment included three drip irrigation treatments under film mulching conditions, i.e., WF (full irrigation), WM (medium irrigation, 70%WF), WL (low irrigation, 40%WF). Results showed the normalized root mean square errors (NRMSEs) for soil water storage (SWS), soil temperature at 0, 5, 10, 20 cm soil depths, leaf area index (LAI), aboveground dry biomass (ADB) and yield under different irrigation treatments for the three seasons by the modified model were 14.8%, 43.5% (an averaged value for the 4 soil depths), 70.5%, 56.4% and 82.1% lower than those of the original model, respectively, which demonstrated higher simulation accuracy of the modified model for mulched field. The modified model could more accurately depict the changes of the SWS and LAI caused by the film mulching at various growth stages. It could also reveal the enhanced soil temperature by film mulching especially at the early stage (0–60 day after sowing (DAS)). We analyzed the film mulching effects by comparing the simulation results of the modified model and the original model which could represent the difference between film mulching and no mulching. Results showed that soil evaporation and evapotranspiration under film mulching conditions were 60.7% and 10.1% lower while crop transpiration was 20.2% higher compared with no mulching. The yield and water use efficiency (WUE) under film mulching conditions improved by 38.9% and 54.3% compared with no mulching. Film mulching had more significant influence on the SWS, LAI, ADB, yield, crop transpiration and WUE under WL treatment than under WF and WM treatments.

Responses of wheat yields and water use efficiency to climate change and nitrogen fertilization in the North China plain

Ensuring food security for the 1.4 billion people of China is a critical challenge, and therefore the accurate assessment of crop yield responses to climate change is a key scientific issue. However, the extent to which the variation in crop growth can be accounted for by the variability in climate variables or by management adaptations remains unclear. Based on daily weather data and management information at six stations, we constructed three sets of simulation experiments using the Crop Environment Resource Synthesis (CERES)-Wheat model. This allowed quantifying the responses of wheat yield and water use efficiency (yield/evapotranspiration, WUE) to climate change and nitrogen (N) fertilization for the period 1981 to 2008 in the North China Plain. Our results indicated that the simulated median values of the wheat yield/WUE decreased (2.62% to 14.26%)/(1.58% to 9.33%) with increasing temperature (T), increased (0.17% to 6.81%)/(0.70% to 4.55%) with elevated CO2 concentration, and changed little with decreasing precipitation in 15 simulation experiments of individual climate variables. Under the combined changes in temperature, N fertilization (T/N), and CO2 concentration, the effects of changes in T/N fertilization on wheat yields and WUE were stronger than the effects of change in CO2 concentration. Interactions between T and CO2 concentration, N fertilization and CO2 concentration appear to play very significant roles in wheat yield. Our study suggests that proper N fertilizer application, changing crop establishment dates, and cultivating new cultivars could be efficient measures for food production prediction and climate change adaptation in the North China Plain. A main result of this work is therefore that proper N application, shifts in crop establishment dates, and the cultivation of new high-temperature tolerant wheat cultivars could contribute safeguarding food security in China, and globally.

Mathematical Integration of Remotely-Sensed Information into a Crop Modelling Process for Mapping Crop Productivity

Remote sensing is a useful technique to determine spatial variations in crop growth while crop modelling can reproduce temporal changes in crop growth. In this study, we formulated a hybrid system of remote sensing and crop modelling based on a random-effect model and the empirical Bayesian approach for parameter estimation. Moreover, the relationship between the reflectance and the leaf area index was incorporated into the statistical model. Plant growth and ground-based canopy reflectance data of paddy rice were measured at three study sites in South Korea. Spatiotemporal vegetation indices were processed using remotely-sensed data from the RapidEye satellite and the Communication Ocean and Meteorological Satellite (COMS). Solar insulation data were obtained from the Meteorological Imager (MI) sensor of the COMS. Reanalysis of air temperature data was collected from the Korea Local Analysis and Prediction System (KLAPS). We report on a statistical hybrid approach of crop modelling and remote sensing and a method to project spatiotemporal crop growth information. Our study results show that the crop growth values predicted using the hybrid scheme were in statistically acceptable agreement with the corresponding measurements. Simulated yields were not significantly different from the measured yields at p = 0.883 in calibration and p = 0.839 in validation, according to two-sample t tests. In a geospatial simulation of yield, no significant difference was found between the simulated and observed mean value at p = 0.392 based on a two-sample t test as well. The fabricated approach allows us to monitor crop growth information and estimate crop-modelling processes using remote sensing data from various platforms and optical sensors with different ground resolutions.

A novel model of water-heat coupling for water-saving irrigated rice fields based on water and energy balance: Model formulation and verification

It is necessary to quantify the hydrothermal processes by develop a water-heat coupling model in rice fields where the transfer between water and energy is dramatically violent, under the circumstance that water-saving irrigation (WSI) was widely used in rice paddy due to the increasing water scarcity. Based on the theory of energy and water balance, considering the interaction of impedance near the ground, a simplified holistic recursive model of water-heat coupling was proposed in the soil–plant–atmosphere continuum (SPAC) system at hourly and daily scales, and the parameters were fitted based on water and energy balance components measured in WSI rice fields. The model, takes various soil moisture conditions into consideration, integrates all the terms in the surface energy balance and the water balance with conventional meteorological data as inputs. Additionally, the model accounts for the specific drying-wetting cycles condition for WSI rice field in the calculation of the canopy resistance (rc) and the recursive process. Variations in crop growth and meteorology were also considered. The results showed that the model performed well in simulating the water and heat fluxes, and temperature profiles during the growth of the rice. This is encouraging for modelling the response of hydrothermal process to drying-wetting cycles or irrigation regimes, which is help for improving the water use efficiency in rice field. Meanwhile, this study indicates that the systemic model is efficient and useful for a better understanding the interaction between hydrological and energy processes in the WSI rice ecosystem.

Plant growth regulators on winter wheat – yield benefits of variable rate application

The use of plant growth regulators (PGRs) on winter wheat in the UK is a common practice. Significant yield benefits can be gained from its use. For the most efficient use of PGRs the rates of application must be matched to the crop canopy. The variation of crop growth was monitored during the spring growing season using vegetation indices derived from satellite imagery. Application rates of PGRs were matched to changes in biomass as measured by the imagery. Final yields were measured using a GPS combine yield meter. The variable application showed an average yield benefit of 0.4 t/ha over the uniform full recommended application rate.

Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data

Knowledge of spatial and temporal variations in crop growth is important for crop management and stable crop production for the food security of a country. A combination of crop growth models and remote sensing data is a useful method for monitoring crop growth status and estimating crop yield. The objective of this study was to use spectral-based biomass values generated from spectral indices to calibrate the AquaCrop model using the particle swarm optimization (PSO) algorithm to improve biomass and yield estimations. Spectral reflectance and concurrent biomass and yield were measured at the Xiaotangshan experimental site in Beijing, China, during four winter wheat-growing seasons. The results showed that all of the measured spectral indices were correlated with biomass to varying degrees. The normalized difference matter index (NDMI) was the best spectral index for estimating biomass, with the coefficient of determination (R2), root mean square error (RMSE), and relative RMSE (RRMSE) values of 0.77, 1.80 ton/ha, and 25.75%, respectively. The data assimilation method (R2 = 0.83, RMSE = 1.65 ton/ha, and RRMSE = 23.60%) achieved the most accurate biomass estimations compared with the spectral index method. The estimated yield was in good agreement with the measured yield (R2 = 0.82, RMSE = 0.55 ton/ha, and RRMSE = 8.77%). This study offers a new method for agricultural resource management through consistent assessments of winter wheat biomass and yield based on the AquaCrop model and remote sensing data.

Sensitivity of the agroecosystem in the Ganges basin to inter-annual rainfall variability and associated changes in land use

The rate of growth in agricultural production has been decreasing in several regions of the world in recent years. The availability of water, which is one of the main inputs, is becoming limiting and more variable. In this article, we study the sensitivity of the agroecosystem to rainfall variability in order to identify vulnerable areas. We applied a longitudinal assessment of remote sensing time-series data, using the correlation between inter-annual rainfall anomalies and anomalies in Normalized Difference Vegetation Index (NDVI), a proxy for crop production. With a novel approach, we then identified whether the sensitivity results from a variation in crop growth or from a deliberate adjustment in the cropping pattern, reflecting a coping strategy. In our case study area, the Ganges basin, 25% of the basin area showed a significant correlation (p < 0.10) between rainfall and NDVI anomalies during the summer monsoon-dominated cropping season, both positive and negative. During the consecutive dry season, 18 % of the basin area showed a significant correlation, mostly positive. This variation in sensitivity shows the added value of spatially explicit information from remote sensing over lumped crop statistics. Primarily in the drier western part of the basin, a coping strategy of increasing fallow land in years with below-average rainfall was detected. Distinguishing a coping strategy from a crop yield reduction is important from both an economic and a hydrologic perspective.

Spatial Variation in Soil Organic Matter, Available Phosphorus, and Potassium under Semi-arid Conditions: Research Station Management Implications

Natural ecosystems are characterized by variability in space and, to a lesser extent, in time. Soil types and properties vary on landscape and field scales. Such variability is either inherent or human-induced and influences crop management. Variability at the plot level impinges on field experimentation. In this study at the main station, Tel Hadya, of the International Center for Agricultural Research in the Dry Areas (ICARDA), we intensively sampled four large field plots and analyzed the samples for soil organic matter (SOM), available Olsen phosphorus (P), and extractable potassium (K). Variability parameters were established for each property, as well as semivariograms. Contour maps for each plot were drawn by kriging. Despite the history of uniform management in each plot, large residual spatial variability was evident for each parameter. Although such variability is generally disregarded by agronomists and crop scientists and is generally believed to be eliminated by an alternate year of uniform cropping (i.e., “homogenization”), the residual persistent variability in these parameters cause variation in crop growth and need to be considered in designing experimental treatments, especially with regard to plot size and orientation. Although P and K variability can be masked by uniform fertilizer application, the effects of SOM persist for several years and thus can influence nutrient availability and soil moisture under such dryland conditions. Systematic soil sampling can help in reducing variability at trial sites, but background variability cannot be eliminated completely in natural bodies such as soils.

Adding Organic Matter Enhanced the Effectiveness of Silicate Rock Fertilizer for Food Crops Grown on Nutritionally Disorder Soils: A Glasshouse Assessment

A glasshouse experiment was carried to identify effects of the application rate of ground silicate rock as a multinutrientfertilizer (SRF) with and without organic matter (OM) on growth and nutrient status of food crops (rice,corn, and soybean). Those crops were grown on 3 different soils in 2 cropping patterns, i.e., rice – soybean and corn– soybean, providing 6 experimental sets. A completely randomized design was applied in each experimental set.The treatment in each set consisted of 3 rates of SRF (5, 10, and 15 g kg-1), those 3 rates + 5 g kg-1 of OM, and acontrol (without adding SRF or OM). The first crops (rice and corn) were grown up to 65 days, while the secondcrop (soybean) was up to 40 days. Results indicated that for crops grown on less fertile soils, the application of SRFonly slightly increased growth of crops, mainly of the 2nd crops, and adding OM greatly increased the growth ofboth the 1st and 2nd crops. In those experimental sets, about 60 – 80% of the variation of crop growth was significantlydetermined by concentration of Cu and several other essential nutrients in crop tissue. In contrast, the growth forcrops grown on more fertile soils was not affected by the application of SRF or/and OM. It was concluded thatadding OM enhanced the effectiveness of SRF as a multi-nutrient fertilizer, and that may be used as an appropriatemulti-nutrient fertilizer or general ameliorant to sustain soil quality and remediate the nutritionally disorder soils.

ESTABLISHING CROP PRODUCTIVITY USING RADARSAT-2

Abstract. Crop productivity is influenced by a number of management and environmental conditions, and variations in crop growth can occur in-season due to, for example, unfavourable meteorological conditions. Consequently information on crop growth must be temporally frequent in order to adequately characterize crop productivity. Leaf Area Index (LAI) is a key indicator of crop productivity and a number of methods have been developed to derive LAI from optical satellite data. Integration of LAI estimates from synthetic aperture radar (SAR) sensors would assist in efforts to monitor crop production through the growing season, particularly during periods of persistent cloud cover. Consequently, Agriculture and Agri-Food Canada has assessed the capability of RADARSAT-2 data to estimate LAI. The results of a sensitivity analysis revealed that several SAR polarimetric variables were strongly correlated with LAI derived from optical sensors for small grain crops. As the growing season progressed, contributions from volume scattering from the crop canopies increased. This led to the sensitivity of the intensity of linear cross-polarization backscatter, entropy and the Freeman-Durden volume scattering component, to LAI. For wheat and oats, correlations above 0.8 were reported. Following this sensitivity analysis, the Water Cloud Model (WCM) was parameterized using LAI, soil moisture and SAR data. A look up table inversion approach to estimate LAI from SAR parameters, using the WCM, was subsequently developed. This inversion approach can be used to derive LAI from sensors like RADARSAT-2 to support the monitoring of crop condition throughout the cropping season.

Relating AEROCam-derived NDVI to apparent soil electrical conductivity (ECa) for corn fields in Wyoming, USA

High-spatial-resolution aerial images are necessary to capture variations in crop growth in small fields. The objective of this study was to analyse the relationship between the normalized difference vegetation index (NDVI), derived from Airborne Environmental Research and Observation Camera (AEROCam; 1 m colour infrared) images, and the apparent soil electrical conductivity (ECa) values measured in furrow- and sprinkler-irrigated fields planted with corn (Zea mays L.) in Lingle, Wyoming. NDVI, calculated using 2005, 2006 and 2007 AEROCam images, and ECa were selected, as both variables are used as a measure of potential crop yield. NDVI values exhibited statistically significant exponential relationships with the ECa values for both fields. For the furrow-irrigated field, the adjusted R 2 values ranged between 0.66 and 0.79, and for the sprinkler-irrigated field the adjusted R 2 values ranged between 0.64 and 0.91. Results obtained in this study indicate that AEROCam images can be used for monitoring crop growth in small fields and can provide valuable insights about crop growth patterns when ECa measurements are not available.

Solar UV-B and UV-A/B exclusion effects on intraspecific variations in crop growth and yield of wheat varieties

UV exclusion studies would help in the assessment of the sensitivity of varieties to ambient level of UV radiation. A field study was conducted to determine the intraspecific responses in relative to sensitivity of Indian wheat ( Triticum aestivum L.) varieties to ambient solar ultraviolet (290–400 nm) radiation. Specifically, the influence of ambient UV radiation on crop growth and yield parameters was investigated by the exclusion of UV radiation form solar spectrum using four Indian wheat varieties; Naveen Chandausi, Swarna, Vidisha and Purna. The plants were grown in specially designed UV exclusion chambers, wrapped with filters that excluded both UV-A/B (<400 nm), UV-B (<315 nm), transmitted all the UV (280–400 nm) or lacked filters. Exclusion of UV significantly enhanced plant height, tiller number, flag-leaf area, dry matter accumulation and yield parameters like number of grains and grain yield in all the four wheat varieties tested. Purna and Vidisha showed more enhancements compared to Swarna and Naveen Chandausi. Sensitivity index showed that all four varieties were UV-sensitive; SI was higher for N. Chandausi and lowest for Purna and it was in the following sequence; Naveen Chandausi > Swarna > Vidisha > Purna. Thus Purna was the most sensitive and Naveen Chandausi the least sensitive to current level of solar UV radiation. The intraspecific differences in sensitivity identified among the wheat varieties imply the options for selecting varieties with increased tolerance to UV-B/UV-A radiations to future attempts in plant breeding.

Development and evaluation of a new Canadian spring wheat sub-model for DNDC

Kröbel, R., Smith, W. N., Grant, B. B., Desjardins, R. L., Campbell, C. A., Tremblay, N., Li, C. S., Zentner, R. P. and McConkey, B. G. 2011. Development and evaluation of a new Canadian spring wheat sub-model for DNDC. Can. J. Soil Sci. 91: 503–520. In this paper, the ability of the DNDC model (version 93) to predict biomass production, grain yield and plant nitrogen content was assessed using data from experiments at Swift Current, Saskatchewan, and St-Blaise, Quebec, Canada. While predicting wheat grain yields reasonably well, the model overestimated the growth of above-ground plant biomass and nitrogen uptake during the first half of the growing season. A new spring wheat sub-model (DNDC-CSW) was introduced with a modified plant biomass growth curve, dynamic plant C/N ratios and modified plant biomass fractioning curves. DNDC-CSW performed considerably better in simulating plant biomass [modeling efficiency (EF): 0.75, average relative error (ARE): 6.0%] and plant nitrogen content (EF: 0.61, ARE: −2.7%) at Swift Current and St-Blaise (EF of 0.75 and ARE of 2.3%), compared with DNDC 93 (biomass SC: EF 0.49, ARE 17.1%, SB: EF 0.02 ARE 33.4%). In comparison with DNDC 93, DNDC-CSW better captured inter-annual variations in crop growth for a range of wheat rotations, increasing the EF from 0.32 to 0.52 for grain and from 0.35 to 0.39 for straw yields. DNDC-CSW also performed considerably better than DNDC 93 in estimating soil carbon changes at Swift Current. Hence, DNDC-CSW has the potential to improve the performance of DNDC 93 in simulating wheat biomass, plant nitrogen, yield and soil carbon at various Canadian sites.

Evaluating high resolution SPOT 5 satellite imagery to estimate crop yield

High resolution satellite imagery has the potential to map within-field variation in crop growth and yield. This study examined SPOT 5 satellite multispectral imagery for estimating grain sorghum yield. A 60 km × 60 km SPOT 5 scene and yield monitor data from three grain sorghum fields were recorded in south Texas. The satellite scene contained four spectral bands (green, red, near-infrared and mid-infrared) with a 10-m spatial resolution. Subsets were extracted from the scene that covered the three fields. Images with pixel sizes of 20 and 30 m were also generated from the individual field images to simulate coarser resolution satellite imagery. Vegetation indices and principal components were derived from the images at the three spatial resolutions. Grain yield was related to the vegetation indices, the four bands and the principal components for each field, and for all the fields combined. The effect of the mid-infrared band on estimates of yield was examined by comparing the regression results from all four bands with those from the other three bands. Statistical analysis showed that the 10-m, four-band image and the aggregated 20-m and 30-m images explained 68, 76 and 83%, respectively, of the variation in yield for all the fields combined. The coefficient of determination between yield and the imagery increased with pixel size because of the smoothing effect. The inclusion of the mid-infrared band slightly improved the R 2 values. These results indicate that high resolution SPOT 5 multispectral imagery can be a useful data source for determining within-field yield variation for crop management.

Examining the Interaction of Growing Crops with Local Climate Using a Coupled Crop–Climate Model

Abstract This paper examines to what extent crops and their environment should be viewed as a coupled system. Crop impact assessments currently use climate model output offline to drive process-based crop models. However, in regions where local climate is sensitive to land surface conditions more consistent assessments may be produced with the crop model embedded within the land surface scheme of the climate model. Using a recently developed coupled crop–climate model, the sensitivity of local climate, in particular climate variability, to climatically forced variations in crop growth throughout the tropics is examined by comparing climates simulated with dynamic and prescribed seasonal growth of croplands. Interannual variations in land surface properties associated with variations in crop growth and development were found to have significant impacts on near-surface fluxes and climate; for example, growing season temperature variability was increased by up to 40% by the inclusion of dynamic crops. The impact was greatest in dry years where the response of crop growth to soil moisture deficits enhanced the associated warming via a reduction in evaporation. Parts of the Sahel, India, Brazil, and southern Africa were identified where local climate variability is sensitive to variations in crop growth, and where crop yield is sensitive to variations in surface temperature. Therefore, offline seasonal forecasting methodologies in these regions may underestimate crop yield variability. The inclusion of dynamic crops also altered the mean climate of the humid tropics, highlighting the importance of including dynamical vegetation within climate models.