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1.
The introduction of a new cultivar in a process-based crop simulation model requires the estimation of cultivar coefficients that define its growth and development characteristics. An accurate estimation of these coefficients requires replicated field experiments that, in many cases, are not available to crop model users. The objective of this study was to employ a pattern recognition approach to estimate cultivar coefficients from a minimum set of experimental data for use with a crop simulation model. The pattern recognition approach is based on similarity measures. Its main goal is to classify groups of data or patterns based on either a priori knowledge or on statistical information extracted from the patterns. Based on the similarity measure as the central calculation of the pattern recognition approach, the algorithm searches the space of features of other cultivars in the database to find the most similar cultivar as the best match to the target cultivar. The approach of this study was based on a few key characteristics of maize crop growth and development, including anthesis and harvest maturity dates, maximum leaf area index (LAImax), final above ground biomass, and grain yield, which were used as the features vector. To construct the feature database, 27,789 hypothetical cultivars were constructed by combining different values of the six cultivar coefficients of the Cropping System Model (CSM)-CERES-Maize. Experiments performed in Florida (FL) and Iowa (IA) USA, Spain, central Punjab, Pakistan, and in Piracicaba, SP, Brazil were selected and later modified to provide a full potential production environment. The crop model was run for potential production for all 27,789 hypothetical cultivars and the outputs of these simulations were used as the feature database. For evaluation of this approach, we used the features for 29 different maize cultivars as reported from field experiments that are available in DSSAT maize cultivar database and also for four additional cultivars of which two had not been used in any aspect of this study. The model was run for all 33 cultivars, using the best match cultivar coefficients, for the conditions of the three study sites and locations where the latter four cultivars have been grown. The simulated crop characteristics were compared with the same simulated crop characteristics based on the original coefficients used to run the simulation model. We found that the approach based on pattern recognition was able to estimate the cultivar coefficients with a reasonable accuracy. The coefficient of determination (r2), root mean square difference (RMSD), and relative root mean square of difference (RMSDr) confirmed that this approach provided reliable estimates for the maize cultivar coefficients. The highest R2 (0.98) was obtained for anthesis in Florida and the lowest (0.57) was obtained for grain yield in Spain. The highest RMSD (8.8) was obtained for maturity in Spain, while the lowest RMSD (1.1) was obtained for aboveground biomass in Florida. Although the values for RMSD were different across the different sites, this approach provided a level of accuracy that might be acceptable, especially for users who only have one year of experimental data and demand the best possible initial guess for the coefficients of their specific cultivar. This approach has been implemented in a simple tool that can be easily applied by users of DSSAT and the CSM-CERES-Maize model.  相似文献   

2.
Appropriate benchmarks for water productivity (WP), defined here as the amount of grain yield produced per unit of water supply, are needed to help identify and diagnose inefficiencies in crop production and water management in irrigated systems. Such analysis is lacking for maize in the Western U.S. Corn Belt where irrigated production represents 58% of total maize output. The objective of this paper was to quantify WP and identify opportunities to increase it in irrigated maize systems of central Nebraska. In the present study, a benchmark for maize WP was (i) developed from relationships between simulated yield and seasonal water supply (stored soil water and sowing-to-maturity rainfall plus irrigation) documented in a previous study; (ii) validated against actual data from crops grown with good management over a wide range of environments and water supply regimes (n = 123); and (iii) used to evaluate WP of farmer's fields in central Nebraska using a 3-y database (2005–2007) that included field-specific values for yield and applied irrigation (n = 777). The database was also used to quantify applied irrigation, irrigation water-use efficiency (IWUE; amount of yield produced per unit of applied irrigation), and the impact of agronomic practices on both parameters. Opportunities to improve irrigation management were evaluated using a maize simulation model in combination with actual weather records and detailed data on soil properties and crop management collected from a subset of fields (n = 123). The linear function derived from the relationship between simulated grain yield and seasonal water supply, namely the mean WP function (slope = 19.3 kg ha−1 mm−1; x-intercept = 100 mm), proved to be a robust benchmark for maize WP when compared with actual yield and water supply data. Average farmer's WP in central Nebraska was ∼73% of the WP derived from the slope of the mean WP function. A substantial number of fields (55% of total) had water supply in excess of that required to achieve yield potential (900 mm). Pivot irrigation (instead of surface irrigation) and conservation tillage in fields under soybean–maize rotation had the greatest IWUE and yield. Applied irrigation was 41 and 20% less under pivot and conservation tillage than under surface irrigation and conventional tillage, respectively. Simulation analysis showed that up to 32% of the annual water volume allocated to irrigated maize in the region could be saved with little yield penalty, by switching current surface systems to pivot, improving irrigation schedules to be more synchronous with crop water requirements and, as a fine-tune option, adopting limited irrigation.  相似文献   

3.
Coefficients of crop cultivars, a required input for the application of crop simulation models, are normally derived from experiments designed specifically for their estimation. This procedure is laborious and time consuming even with a reduced data set. Recent studies have shown that cultivar coefficients for soybean lines can be derived from standard crop performance trials. However, this needs to be confirmed in other crops and be simplified for broader applications. The objective of this study was to determine the feasibility of estimating cultivar coefficients for new peanut lines using data from standard performance trials. Data from performance trials of 17 peanut lines that were conducted in farmers’ fields and research stations in the northeastern and northern regions of Thailand during 2002–2004, totaling eight environments, were used in this study. The data that were collected included dates of first flower and harvest maturity, final biomass, pod and seed yield, seed size, pod and seed harvest index, and shelling percentage. These data were used for the calculation of the cultivar coefficients using the Genotype Coefficient Calculator (GENCALC) program, which is part of the Decision Support System for Agrotechnology Transfer (DSSAT). Evaluation of the derived cultivar coefficients was conducted with time series growth data collected in three additional experiments grown during the 2002 rainy, 2003 dry, and 2004 dry seasons. The model calibration with GENCALC resulted in cultivar coefficients that produced simulated values for the development and growth characteristics that were close to their corresponding observed values, with root mean square errors (RMSE) ranging from 1.5 to 4.1 days for development traits and 0.20–1.32 t ha−1 for growth traits and coefficient of determinations (r2) ranging from 0.55 to 0.97 for all traits. The evaluation of the cultivar coefficients that were derived from the performance trials data with independent data worked well for all development traits and fairly well for the plant growth characteristics, as judged by RMSE, r2, normalized root mean square error (RMSEn) and index of agreement (d). The mean RMSE values for days to first flower and to harvest maturity were 1.6 and 2.4 days; and mean r2 were 0.72 and 0.91, respectively. The mean RMSEn values calculated from time series growth data were 17.9, 24.6 and 11.5% with the mean d values of 0.88, 0.93 and 0.93 for the 2002 rainy, 2003 dry and 2004 dry seasons, respectively. It is concluded that the cultivar coefficients of peanut lines can be estimated from typical data that are collected in standard performance trials using either GENCALC or similar methodologies.  相似文献   

4.
The relationship between agricultural water demand and supply has been of interest to government decision makers and scientists because of its importance in water resources management. We developed a water cycle model for eastern Eurasia that can estimate water requirements for crop growth and evaluate the demand–supply relationships of agricultural water use on a continental scale. To produce an appropriate water cycle, the model was constructed based on small drainage basins. To validate the model performance with respect to simulated runoff, which is here considered as the available water resource, we compared our outputs with those of other models and with observed river discharges. The results show that this model is comparable to other models and that it is applicable for the evaluation of water cycles at continental scale. We defined two types of crop water deficits (CWDs) as indicators of agricultural water demand. These were formulated by considering the physical processes of crop water use; we did not include water consumption that is dependent on cultivation management practices, such as water losses in irrigation systems. We assessed the reliability of our indicators by comparison with indicators from other studies and with published statistics related to agricultural water use. These comparisons suggest that our indicators are consistent with independent data and can provide a reasonable representation of water requirements for crop growth.  相似文献   

5.
In saline fields, irrigation management often requires understanding crop responses to soil moisture and salt content. Developing models for evaluating the effects of soil moisture and salinity on crop yield is important to the application of irrigation practices in saline soil. Artificial neural network (ANN) and multi-linear regression (MLR) models respectively with 10 (ANN-10, MLR-10) and 6 (ANN-6, MLR-6) input variables, including soil moisture and salinity at crop different growth stages, were developed to simulate the response of sunflower yield to soil moisture and salinity. A connection weight method is used to understand crop sensitivity to soil moisture and salt stress of different growth stages. Compared with MLRs, both ANN models have higher precision with RMSEs of 1.1 and 1.6 t ha−1, REs of 12.0% and 17.3%, and R2 of 0.84 and 0.80, for ANN-10 and ANN-6, respectively. The sunflower sensitivity to soil salinity varied with the different soil salinity ranges. For low and medium saline soils, sunflower yield was more sensitive at crop squaring stage, but for high saline soil at seedling stage. High soil moisture content could compensate the yield decrease resulting from salt stress regardless of salt levels at the crop sowing stage. The response of sunflower yield to soil moisture at different stages in saline soils can be understood through the simulated results of ANN-6. Overall, the ANN models are useful for investigating and understanding the relationship between crop yield and soil moisture and salinity at different crop growth stages.  相似文献   

6.
A field experiment using system of rice intensification (SRI) techniques was conducted in Chiba, Japan during the 2008 rice-growing season (May–September) with eight treatment combinations in a split–split plot design (S–SPD) to observe the potential of SRI methods under the temperate climatic conditions in Japan. Intermittent irrigation with alternate wetting and drying intervals (AWDI) and continuous flooding throughout the cropping season were the two main-plot factors, while the effects of age of seedlings and plant spacing were evaluated as sub and sub–sub plot factors, respectively. The experiment results revealed that the proposed AWDI can save a significant amount of irrigation water (28%) without reduced grain yield (7.4 t/h compared with 7.37 t/h from normal planting with ordinary water management). Water productivity was observed to be significantly higher in all combinations of practices in the intermittent irrigation plots: 1.74 g/l with SRI management and AWDI as compared to 1.23 g/l from normal planting methods with ordinary water management. In addition, the research outcomes showed a role of AWDI in minimizing pest and disease incidence, shortening the rice crop cycle, and also improving plant stand until harvest. Synergistic effects of younger seedlings and wider spacing were seen in tillering ability, panicle length, and number of filled grains that ultimately led to higher productivity with better grain quality. However, comparatively better crop growth and yields when using the same SRI practices with ordinary water management underscore a need for further investigations in defining what constitute optimum wetting and drying intervals considering local soil properties, prevailing climate, and critical watering stages in rice crop management.  相似文献   

7.
《Field Crops Research》1999,62(1):85-95
Crop simulation models are receiving increasing use in agriculture and are recommended as multipurpose tools in research and farm management. Of one particular interest to crop growers is the possibility of applying crop models for real-time yield forecasting. This investigation evaluated the utility of the SUCROS model for site-specific real-time crop biomass and grain-yield forecasting. A stochastic forecasting approach was used combining generated weather data with observed data for model updating. The forecast procedure was tested with field data collected at four sites in the UK over two growing seasons. The results showed that across all site-years, the model is able to forecast the final biomass and grain yield with <10% bias. There was no significant difference between observed and forecasted biomass and grain yield for forecasts made at anthesis or milky grain stage although earlier forecasts did show significant differences. The ranking of the observed and forecast biomass and grain yield were also highly correlated for the later forecasts.  相似文献   

8.
Optimization of irrigation water is an important issue in agricultural production for maximizing the return from the limited water availability. The current study proposes a simulation–optimization framework for developing optimal irrigation schedules for rice crop (Oryza sativa) under water deficit conditions. The framework utilizes a rice crop growth simulation model to identify the critical periods of growth that are highly sensitive to the reduction in final crop yield, and a genetic algorithm based optimizer develops the optimal water allocations during the crop growing period. The model ORYZA2000, which is employed as the crop growth simulation model, is calibrated and validated using field experimental data prior to incorporating in the proposed framework. The proposed framework was applied to a real world case study of a command area in southern India, and it was found that significant improvement in total yield can be achieved by the model compared to other water saving irrigation methods. The results of the study were highly encouraging and suggest that by employing a calibrated crop growth model combined with an optimization algorithm can lead to achieve maximum water use efficiency.  相似文献   

9.
Rice planthoppers' damage on Pusa Basmati 1 cultivar was simulated with InfoCrop, a generic crop growth simulation model. The model was calibrated and validated with two experimental data sets on planthopper population and rice yield that were generated through differential insecticide application during the rainy season 2006 and 2007. Simulated yield and total dry matter (TDM) in various treatments over the two experiments were found to be proximal to the observed yields (R2 = 0.972, RMSE = 4.61%) and TDM (R2 = 0.949, RMSE = 3.25%), respectively. Likewise, the simulated yield and TDM losses were also respectively close to observed yield losses (R2 = 0.938, RMSE = 13.53%) and TDM losses (R2 = 0.835, RMSE = 19.12%), suggesting appropriate validation of planthopper damage mechanism on Pusa Basmati 1 rice. Economic injury levels (EILs) of planthoppers were simulated with two control expenditures involving two applications with each of monocrotophos and imidacloprid, and three market prices of Pusa Basamti 1 rice. The EIL exhibited a negative relationship with market value of produce but a positive one with expenditure on control measures. Simulated EILs were comparable to earlier established empirical EILs, indicating utility of simulation models for developing location specific EILs that may help in doing away with the use of blanket EILs. Iso-loss curves, devised through validated model, depicted combinations of crop age and planthopper population that resulted in similar yield losses. Both the EILs and iso-loss curves can be useful in monitoring planthopper populations and promoting judicious pesticide applications that would avoid unwarranted control expenditure and environmental contamination. The simulation models being based on detailed crop ecological and physiological processes and pest damage mechanism can thus aid in development of location-specific decision support tools and ensure precision in pest management decisions.  相似文献   

10.
There has been an increasing interest to employ crop growth simulation models for taking decision on irrigation water management. The effectiveness of such decisions mainly lies on the efficiency of the model in simulating the crop growth and the yield, which are influenced by the value of the parameters of the model. Therefore, calibration of such models is necessary before it can be employed for any application. This study proposes an auto-calibration procedure for ORYZA2000, a rice crop growth simulation model, for its application in South India. The data employed for calibration is taken from a field experiment conducted for 2 years in an experimental farm in South India. The ORYZA2000 model was integrated within Genetic Algorithm optimizer, which calls the simulator during each generation to evaluate the objective function. The auto-calibrated model was tested for its performance using a validation data set from the same experimental data. The results showed that the calibrated ORYZA2000 model is capable of simulating the full irrigation and water stress condition of rice crop effectively, and can be used to develop deficit irrigation management schedules.  相似文献   

11.
Vegetative and reproductive growth information of lesquerella (Physaria fendleri), a new oilseed crop targeted for bio-products, is important to understand especially in the early commercialization stage of this new crop. The objective of this study was to determine the effect of fall, winter, and spring planting dates over three years on the ontogeny of the crop including biomass, floral buds, flowers, and siliques. Fall plantings always produced more than the other plantings due to the extended season. Winter and spring plantings had less biomass and produced fewer buds, flowers, and siliques. The compensation of lower crop management costs and a shorter growing season could make winter a viable option for planting in the southwest. Spring planting could become viable if seed shatter due to summer rains could be reduced. The information will help decide growing regions suitable for crop production and determine what reproductive stages could be manipulated to improve seed yields.  相似文献   

12.
Wheat cropping systems and technologies in China   总被引：1，自引：0，他引：1  
Chinese wheat (Triticum aestivum) production has developed rapidly during the last 57 years, largely due to improved crop management technologies and new varieties. The history of wheat planting technologies in China was reviewed, and the physiological mechanisms that allow wheat to attain high yield under these planting systems were analyzed. The use of leaf number and stage of development to indicate the optimum timing for applications of fertilizers and irrigation water, and uniform seeding at reduced seeding rates to control lodging contributed significantly to the substantial progress in wheat productivity. However, flood irrigation and tillage-based practices also resulted in serious problems, including a decline in soil fertility and quality, environmental pollution, and inefficient use of water resources. The major future challenges facing wheat production are to improve water and nutrient use efficiency. Conservation agriculture-based resource conservation technologies such as zero or reduced tillage, flat or raised bed-planting systems, and rational management of crop residues to eliminate burning in the field are among the strategies we strongly recommend for improving agricultural environments and stabilizing/increasing wheat production in China.  相似文献   

13.
Quantifying the exploitable gap between average farmer yields and yield potential (YP) is essential to prioritize research and formulate policies for food security at national and international levels. While irrigated maize accounts for 58% of total annual maize production in the Western U.S. Corn Belt, current yield gap in these systems has not been quantified. Our objectives were to quantify YP, yield gaps, and the impact of agronomic practices on both parameters in irrigated maize systems of central Nebraska. The analysis was based on a 3-y database with field-specific values for yield, applied irrigation, and N fertilizer rate (n = 777). YP was estimated using a maize simulation model in combination with actual and interpolated weather records and detailed data on crop management collected from a subset of fields (n = 123). Yield gaps were estimated as the difference between actual yields and simulated YP for each field-year observation. Long-term simulation analysis was performed to evaluate the sensitivity of YP to changes in selected management practices. Results showed that current irrigated maize systems are operating near the YP ceiling. Average actual yield ranged from 12.5 to 13.6 Mg ha−1 across years. Mean N fertilizer efficiency (kg grain per kg applied N) was 23% greater than average efficiency in the USA. Rotation, tillage system, sowing date, and plant population density were the most sensitive factors affecting actual yields. Average yield gap was 11% of simulated YP (14.9 Mg ha−1). Time trends in average farm yields from 1970 to 2008 show that yields have not increased during the past 8 years. Average yield during this period represented ∼80% of YP ceiling estimated for this region based on current crop management practices. Simulation analysis showed that YP can be increased by higher plant population densities and by hybrids with longer maturity. Adoption of these practices, however, may be constrained by other factors such as difficulty in planting and harvest operations due to wet weather and snow, additional seed and grain drying costs, and greater risk of frost and lodging. Two key points can be made: (i) irrigated maize producers in this region are operating close to the YP ceiling and achieve high levels of N use efficiency and (ii) small increases in yield (<13%) can be achieved through fine tuning current management practices that require increased production costs and higher risk.  相似文献   

14.
Sugarcane crops are managed over 8 million hectares in Brazil and future extensions might occur on less favorable lands where irrigation would be necessary to increase and stabilize yields. Root growth was studied by sequential soil coring under rainfed and irrigated conditions for one cultivar widely planted in Brazil. Root length densities (RLD) were measured 34, 49, 125, 179, 241 and 322 days after planting (DAP) down to a depth of 1 m. At the harvest (332 DAP), root intersects (a proxy for RLD) were counted on two vertical trench walls in each water supply regime, down to a depth of 6.0 m. The highest RLD in deep layers (below a depth of 0.6 m) were observed in the rainfed crop from 125 DAP onwards. By contrast, the highest RLD in the upper layers during dry periods were found in the irrigated crop. The maximum depth reached by roots at the harvest was little affected by irrigation: 4.70 m and 4.25 m in the rainfed and irrigated crop, respectively. About 50% of root intersects were observed below the depth of 1 m in the two water supply regimes. This pattern suggested a strong genetic control of root growth in deep soil layers. The total amount of root intersects 332 DAP was 49% higher in the rainfed crop than in the irrigated crop, and root distribution was more homogeneous. Mean root front velocity was about 0.5 cm day−1 the first 4 months after planting and increased thereafter up to the end of the harvest (1.86 cm day−1 and 1.75 cm day−1 on average in the rainfed and the irrigated crops, respectively). Our study pointed out the necessity to take into account the development of sugarcane roots in deep soil layers to improve our understanding of net primary production control by water availability.  相似文献   

15.
Multivariate global sensitivity analysis for dynamic crop models   总被引：2，自引：0，他引：2  
Dynamic crop models are frequently used in ecology, agronomy and environmental sciences for simulating crop and environmental variables at a discrete time step. They often include a large number of parameters whose values are uncertain, and it is often impossible to estimate all these parameters accurately. A common practice consists in selecting a subset of parameters by global sensitivity analysis, estimating the selected parameters from data, and setting the others to some nominal values. For a discrete-time model, global sensitivity analyses can be applied sequentially at each simulation date. In the case of dynamic crop models, simulations are usually computed at a daily time step and the sequential implementation of global sensitivity analysis at each simulation date can result in several hundreds of sensitivity indices, with one index per parameter per simulation date. It is not easy to identify the most important parameters based on such a large number of values. In this paper, an alternative method called multivariate global sensitivity analysis was investigated. More precisely, the purposes of this paper are (i) to compare the sensitivity indices and associated parameter rankings computed by the sequential and the multivariate global sensitivity analyses, (ii) to assess the value of multivariate sensitivity analysis for selecting the model parameters to estimate from data. Sequential and multivariate sensitivity analyses were compared by using two dynamic models: a model simulating wheat biomass named WWDM and a model simulating N2O gaseous emission in crop fields named CERES-EGC. N2O measurements collected in several experimental plots were used to evaluate how parameter selection based on multivariate sensitivity analysis can improve the CERES-EGC predictions.The results showed that sequential and multivariate sensitivity analyses provide modellers with different types of information for models which exhibit a high variability of sensitivity index values over time. Conversely, when the parameter influence is quite constant over time, the two methods give more similar results. The results also showed that the estimation of the parameters with the highest sensitivity indices led to a strong reduction of the prediction errors of the model CERES-EGC.  相似文献   

16.
《Field Crops Research》1988,19(1):63-74
Crop simulation may provide an inexpensive means to evaluate the feasibility of different cropping practices to optimize productivity and profitability. One practice, ratoon-cropping, may increase productivity and reduce per-unit production costs associated with conservation tillage farming systems in tropical and subtropical regions. sorkam, a dynamic plant growth model for grain sorghum [Sorghum bicolor (L.) Moench], was used to evaluate the potential of rainfed ratoon grain sorghum over diverse climatic regions of Texas. Eleven independent data sets collected in the U.S.A. from sites in Georgia and Texas were used to determine the model's accuracy. The model produced realistic estimates of grain yield for planted, ratoon, and combined (planted + ratoon) crops. Simulated grain yields usually were within 25% of the observed yield for the planted, ratoon, and combined crops with cultivars that produced the highest ratoon grain yield at each location. Ratoon grain yield results of multi-year simulations (10–30 years) from 14 locations over the eastern half of Texas using historic, location-specific, meteorological data indicated that the probability of obtaining ratoon grain yield > 3.0 Mg/ha was confined to the upper coastal plain region of Texas. The area best suited for rainfed ratoon grain sorghum appeared to be confined south and east of a line running from west of Corpus Christi to Beeville to College Station to west of Center, Texas. Use of crop models can play an important role in identifying strengths and weaknesses of potential cropping systems when used in combination with historical climatic data and/or computer weather generators.  相似文献   

17.
Summary

Nitrogen (N) availability for crop uptake is dependent on various factors that influence the transformation of N sources and transport of N forms in soils. The fate and transport of N is site specific. Therefore evaluation of N dynamics under each condition is neither practical nor feasible. Simulation models which are adequately calibrated and tested can be used to estimate the fate and transport of N as well as crop responses under different production systems. These evaluations provide some guidelines as how to manage N and water efficiently to maximize the N uptake efficiency and minimize the losses. Thus, they contribute to the development of N and water best management practices. In this chapter, we discuss recent information on experimentally measuring the water and nutrient transport in soils as well as performing estimations using simulation models. The development and application of different simulation models for different production systems have been summarized. Some case studies on nitrogen and water best management practices are also discussed.  相似文献   

18.
《Field Crops Research》2004,89(1):27-37
In water-limited environments soil water content at sowing is important in determining durum wheat germination, emergence and plant establishment. Soil water content interacts greatly with soil nitrogen content, affecting nitrogen uptake and crop productivity. Simulation models can be used to confirm the optimal strategy by testing several crop management scenarios.The CERES-Wheat model, previously calibrated and validated in southern Italy, has been used in a seasonal analysis to optimise nitrogen fertilisation of durum wheat at different levels of crop available water (CAW) at planting date in southern Italy. The simulation was carried out for a 48-year period with measured daily climatic data. The 99 simulated scenarios derived from the combinations of different CAW levels at sowing, nitrogen fertiliser rates and application times.The results obtained from the simulation indicated that the effect of CAW at sowing was relevant for durum wheat production at lowest and highest values, while the optimal sowing time to maximise yield and profit can be considered when CAW is 40–60%. In the case study optimal N fertiliser amount was estimated to be 100±20 kg ha−1, from a productive, environmental and economic point of view. The nitrogen split application—half at sowing and half at stem extension stage—resulted in the best management practice.This application of the CERES-Wheat model confirmed the capability of the model to compare several crop management strategies in a typical durum wheat cropping area.  相似文献   

19.
New tools in agricultural research are needed for improved assessment of agronomic practices and their impacts on crop production. Remote sensing data acquired by satellite sensors offers great promise to complement field-based approaches, which generally suffer from small sample sizes. In this study, we used Landsat data from the Yaqui Valley, a prominent spring wheat (Triticum aestivum L.) growing region in Northwest Mexico, to investigate the effect of planting date and fallow period weeds on wheat yields. Three crops cycles were analyzed for the planting date study, while 2 years were investigated for the weed study.  相似文献   

20.
Accurate forecasts of daily crop evapotranspiration (ETc) are essential for real-time irrigation management and water resource allocation. This paper presents a method for the short-term forecasting of ETc using a single-crop coefficient approach and public weather forecasts. Temperature forecasts with a 7-day lead time in 2013–2015 were retrieved and entered into a calibrated Hargreaves–Samani model to compute daily reference evapotranspiration (ET0) forecasts, while crop coefficient (Kc) empirical values were estimated from both observed ETc value and calculated ET0 values using the Penman–Monteith equation for the period of 2010–2012. Daily ETc forecasts of irrigated double-cropping rice were determined for three growing seasons during the period of 2013–2015 and were compared with ETc values measured by the weighing lysimeters at the Jiangxi experimental irrigation station in southeastern China. During the early rice season, the average mean absolute error (MAE) and root-mean-square-error (RMSE) values of ETc forecasts ranged from 0.95 to 1.06 mm day?1 and from 1.18 to 1.31 mm day?1, respectively, and the average correlation coefficient (R) ranged from 0.39 to 0.54; for late rice, the average MAE and RMSE values ranged from 1.01 to 1.09 mm day?1 and from 1.32 to 1.40 mm day?1, respectively, and the average R value ranged from 0.54 to 0.58. There could be three factors responsible for errors in ETc forecasts, including temperature forecast errors, Kc value errors and neglected meteorological variables in the HS model, including wind speed and relative humidity. In addition, ETc was more sensitive to changes in temperature than Kc. The overall results indicated that it is appropriate to forecast ETc with the proposed model for real-time irrigation management and water resource allocation.  相似文献   

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