Long-term Weather Data Research Articles

Reliability of production of quick to medium maturity maize in areas of variable rainfall in north-east Australia

Maize may assume a more significant role in grain crop production systems in north-east Australia if the probability of producing low yields associated with given amounts of available water can be reduced. Growing hybrids with very early maturity provides a possible way to achieve this. Simulation studies of dryland maize production in areas of highly variable rainfall in north-east Australia were undertaken using long-term weather data input to the APSIM model configured for quick to medium maturity maize. The studies focussed on sowing time options, population density, cultivars, and water availability at sowing. Simulation outputs included predicted mean and median yield, measures of yield variability, and the probability of producing low to very low yield (<2 t/ha). The study showed that optimum sowing date varied with location, and that low populations gave more reliable production, despite some potential yield losses in favourable years. The results of the simulation study provide estimates of yield and thus economic viability of maize production that are interpreted in terms of seasonal variability. They indicate that maize is a viable dryland cropping option provided that cultivar, sowing time and starting water conditions are optimised. Non-optimal conditions of water supply at sowing should be avoided, as greater variability in yield and reduced viability are predicted.

Assessing crop management options with crop simulation models based on generated weather data

The efficient use of crop simulation models is an effective complement to experimental research. Long-term weather data obtained at a specific site are normally required for the application of these crop simulation models to help determine alternate management practices and associated decisions. Stochastic weather generators sometimes are used to complement or substitute historical weather data. The objective of this study was to evaluate the suitability of weather data generated by the weather generators WGEN and SIMMETEO as input for crop simulation models in order to determine the best option(s) among a number of different crop management practices. Five locations across Iran representing different climates were selected. The wheat, maize, and soybean models of the Decision Support System for Agrotechnology Transfer (DSSAT) were applied in this study, using 30 years of observed weather data and 90 years of weather data generated by WGEN and SIMMETEO. Simulated grain yield using either observed weather data or weather data generated by WGEN and SIMMETEO in response to various ‘experimental’ factors, e.g., cultivar selection, planting date, planting density, irrigation threshold, and change in precipitation under irrigated and rainfed conditions were compared. The statistical evaluation was based on t, F, and Kolomogrov-Smirnov (K-S) tests. The average of the percentage rejected tests was 20% and the parameter estimation method had no impact on the number of rejected tests. Irrespective of some significant differences between simulated yield based on observed weather data and those based on weather data generated by WGEN and SIMMETEO, a similar conclusion could be drawn about the best cultivar, planting date, plant density and irrigation threshold and response to changes in the amount of precipitation. Based on the results of this study it can be concluded that for many crop model applications where only relative estimates or determination of the best management option(s) rather than absolute values are required, weather data generated by either WGEN and SIMMETEO are accurate and sufficient.

Predictions of invasion success of Gonatocerus triguttatus (Hymenoptera: Mymaridae), an egg parasitoid of Homalodisca vitripennis (Hemiptera: Cicadellidae), in California using life table statistics and degree–day values

The number of expected generations of Gonatocerus triguttatus Girault, a parasitoid of the glassy-winged sharpshooter, Homalodisca vitripennis (Germar), in California (USA) was estimated using life table statistics and degree–day requirements. Between 0–18.9 and 0– 25.3 generations per year were estimated across different climatic regions in California, using life table and degree–day models, respectively. Temperature-based values for net reproductive rate, R0, were estimated in California using a laboratory-derived equation and ranged from 0 to approximately 29.4 and analyses indicate that a minimum of 7–7.8 generations (calculated using life table and degree–day models) are required each year to sustain a population of G. triguttatus in a given area. Long-term weather data from 381 weather stations across California were used with an Inverse-Distance Weighting algorithm to map various temperature-based demographic estimates for G. triguttatus across the entire state of California. This Geographic Information Systems model was used to determine number of G. triguttatus generations based on degree–day accumulation, generation time, Tc, and Ro. GIS mapping indicated that the only areas in California that may have climatic conditions favorable for supporting the permanent establishment of invading populations of G. triguttatus, should H. vitripennis successfully establish year-round populations, are Imperial, San Diego, Riverside, Orange and the southern areas of Santa Barbara, Ventura, Los Angeles and San Bernardino counties. Northern counties in California that experience cooler average year-round temperatures do not appear to be conducive to the establishment of permanent populations of G. triguttatus. The mechanisms facilitating G. triguttatus invasion and the implications of these temperature-based estimates for biological control of H. vitripennis are discussed. Published by Elsevier Inc.

Influence of climate on winter wheat productivity in different climate regions of China, 1961–2000

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 32:219-227 (2006) - doi:10.3354/cr032219 Influence of climate on winter wheat productivity in different climate regions of China, 1961–2000 Yanling Song1,2,*, Deliang Chen3,4, Wenjie Dong3 1Chinese Academy of Sciences Key Laboratory of Regional Climate–Environment Research for Temperate East Asia, START Regional Center for Temperate East Asia, Institute of Atmospheric Physics, Qijiahuozi Road, Chaoyang, Beijing 100029, China 2Graduate School of the Chinese Academy of Sciences, 19 Yuquan Road, Shijingshan, Beijing 100049, China 3National Climate Center, China Meteorological Administration, 46 Zhongquancun Road, Beijing 100081, China 4Earth Sciences Centre, University of Gothenburg, Box 460, 405 30 Göteborg, Sweden *Email: anna_song74@yahoo.com.cn ABSTRACT: The World Food Studies crop model (WOFOST) was tuned and validated with observed meteorological data as well as winter wheat growth and yield data for 50 stations in 12 provinces in China from 1998 to 2003. The results show that most of the simulated growth dates and yields lie within ±15% range of the observed data, showing that the genetic parameters determined are reasonable in most winter wheat regions of China. Overall, the results demonstrate that the WOFOST model predicts potential winter wheat yields (assuming there are no limits imposed by water availability) and growth reasonably well under current climatic conditions. By assuming winter wheat varieties and agricultural practices stay constant, the parameterization obtained by the tuning was then used to model the impacts of climate on winter wheat growth for the 50 stations using long-term weather data from 1961 to 2000. Over this period, the simulated potential yield of winter wheat in northern China increased by 2.3%, while decreasing by 1.6% in southern China. In northern China, the accumulated negative temperatures during winter increased by 29.3°C decade–1, which is most likely the main reason for the increased potential yield of winter wheat. In southern China, one possible reason for the decreased potential yield is a shortened growth interval for winter wheat caused by the increasing mean temperatures. KEY WORDS: Crop growth model · WOFOST · Climate change · Winter wheat · China Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 32, No. 3. Online publication date: October 26, 2006 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2006 Inter-Research.

Can Abundant Summer Precipitation Counter Losses in Herbage Production Caused by Spring Drought?

Drought is an inherent trait of most rangelands and sound management necessitates managers address two fundamental questions when facing a drought situation. The first question is, ‘‘what is the probability that a useful amount of precipitation will be received over the period of concern?’’ and the second question is, ‘‘if it does rain, what will the impact be in terms of quantity and quality of herbage produced?’’ The objective of this study was to address the second question. Our hypothesis was that herbage growth response to above normal summer precipitation (i.e., 23 in July and August) would be limited in the northern Great Plains because of a general absence of productive warm-season species. Study plots were twelve 5 3 10-m nonweighing lysimeters. Treatments were: 1) simulated (i.e., rainout shelter imposed), severe spring drought (i.e., 1 May ‐ 1 July) followed by ambient precipitation thereafter; 2) simulated, severe spring drought followed by ambient precipitation thereafter plus summer irrigation (i.e., July and August); 3) ambient precipitation only; and 4) ambient precipitation plus summer irrigation. Results indicated substantial herbage production can be expected in this region during summer when precipitation is well above average because of the positive growth response of blue grama (Bouteloua gracilis [H.B.K.] Lag. ex Griffiths), the dominant warm-season grass growing in this region. However, results also showed that level of production in the study situation (i.e., spring drought, wet summer) was only about 50% of that attained in a normal (i.e., wet spring/dry summer) year. Moreover, long-term weather data shows the probability of receiving 23 normal precipitation in both July and August (i.e., our irrigation treatments) is , 1%. Thus, although these rangelands possess the capacity to respond favorably to summer precipitation, the low probability of receiving substantial levels of summer precipitation ensures levels of ecological and economic risk remain high.

Use of life table statistics and degree-day values to predict the invasion success of Gonatocerus ashmeadi (Hymenoptera: Mymaridae), an egg parasitoid of Homalodisca coagulata (Hemiptera: Cicadellidae), in California

Abstract Life table statistics and degree-day requirements for Gonatocerus ashmeadi Girault, a parasitoid of the glassy-winged sharpshooter Homalodisca coagulata (Say), were used to estimate the number of expected parasitoid generations in California (USA). Between two to 51 and one to 37 generations per year were estimated across different climatic regions in California, using life table and degree-day models, respectively. Temperature-based values for net reproductive rate, R o , were estimated in California using a laboratory-derived equation and ranged from zero to approximately 48 and analyses indicate that a minimum of eight generations are required each year to sustain a population increase of G. ashmeadi . Long-term weather data from 381 weather stations across California were used with an Inverse-Distance Weighting algorithm to map temperature-based estimations for the entire state of California. This Geographic Information Systems model was used to determine number of G. ashmeadi generations based on day-degree accumulation, T c , and R o . GIS mapping indicated that Californian counties in the north, central west coast, central west and Sierra Nevada regions may be climatic conditions unfavorable for supporting the permanent establishment of invading populations of G. ashmeadi should H. coagulata successfully establish year-round populations in these areas. Southern counties in California that experience warmer year round temperatures and support year round populations of H. coagulata , appear to be conducive to the establishment of permanent populations of G. ashmeadi . The mechanisms facilitating G. ashmeadi invasion and the implications of these temperature-based estimates for biological control of H. coagulata are discussed.

Analysis of the Inter-Annual Variation of Peanut Yield in Georgia Using a Dynamic Crop Simulation Model

It is common practice to use crop simulation models and long-term weather data to study the impact of climate variability on yield. Simulated yields mainly reflect the weather variability but not the adoption of new technologies; both sources of variation are reflected in long-term observed yields. Therefore, long-term observed yields, if available, cannot be readily used for evaluation of crop models. The objectives of this study were to analyze the impact of climate variability on long-term historical peanut yield in Georgia obtained with a dynamic crop simulation model and to assess the applicability of using long-term average county yield determined from statistical estimates for evaluation of the simulated yield. Observed yields obtained from state variety trials as well as yield estimates from the USDA-NASS for three counties in the Georgia peanut belt from 1934 to 2003 were used for evaluating simulated yield series. Simulated yields based on the CSM-CROPGRO-Peanut model were categorized into three technological periods (TP). A weighted average based on the acreage of the soil type, the peanut type, and the irrigated land in each county was calculated to obtain a unique simulated yield. Then yields and weather data of the 70-year period were grouped with respect to El Nio Southern Oscillation phases and TPs. Pearson's coefficient of correlation, the least significant difference (LSD), and the t-test were used to evaluate the results. When compared with observed yields, NASS estimates failed to estimate the weather variability at the beginning of the period, but simulated yields clearly reflected that variability during the 70-year period. NASS yield estimates seemed to be useful for evaluating simulated yields from the mid-1970s. The results showed that crop models can be useful in understanding the inter-annual variation of yield due to climate variability if appropriate adjustments are made to account for changes and improvements in agrotechnology.

Can Abundant Summer Precipitation Counter Losses in Herbage Production Caused by Spring Drought?

Drought is an inherent trait of most rangelands and sound management necessitates managers address two fundamental questions when facing a drought situation. The first question is, ‘‘what is the probability that a useful amount of precipitation will be received over the period of concern?’’ and the second question is, ‘‘if it does rain, what will the impact be in terms of quantity and quality of herbage produced?’’ The objective of this study was to address the second question. Our hypothesis was that herbage growth response to above normal summer precipitation (i.e., 23 in July and August) would be limited in the northern Great Plains because of a general absence of productive warm-season species. Study plots were twelve 5 X 10-m non-weighing lysimeters. Treatments were: 1) simulated (i.e., rainout shelter imposed), severe spring drought (i.e., 1 May - 1 July) followed by ambient precipitation thereafter; 2) simulated, severe spring drought followed by ambient precipitation thereafter plus summer irrigation (i.e., July and August); 3) ambient precipitation only; and 4) ambient precipitation plus summer irrigation. Results indicated substantial herbage production can be expected in this region during summer when precipitation is well above average because of the positive growth response of blue grama (Bouteloua gracilis [H.B.K.] Lag. ex Griffiths), the dominant warm-season grass growing in this region. However, results also showed that level of production in the study situation (i.e., spring drought, wet summer) was only about 50% of that attained in a normal (i.e., wet spring/dry summer) year. Moreover, long-term weather data shows the probability of receiving 23 normal precipitation in both July and August (i.e., our irrigation treatments) is < 1%. Thus, although these rangelands possess the capacity to respond favorably to summer precipitation, the low probability of receiving substantial levels of summer precipitation ensures levels of ecological and economic risk remain high.

Near-Ground Remote Sensing of Green Area Index on the Shortgrass Prairie

Drought is an inherent trait of most rangelands and sound management necessitates managers address two fundamental questions when facing a drought situation. The first question is, ‘‘what is the probability that a useful amount of precipitation will be received over the period of concern?’’ and the second question is, ‘‘if it does rain, what will the impact be in terms of quantity and quality of herbage produced?’’ The objective of this study was to address the second question. Our hypothesis was that herbage growth response to above normal summer precipitation (i.e., 23 in July and August) would be limited in the northern Great Plains because of a general absence of productive warm-season species. Study plots were twelve 5 X 10-m non-weighing lysimeters. Treatments were: 1) simulated (i.e., rainout shelter imposed), severe spring drought (i.e., 1 May - 1 July) followed by ambient precipitation thereafter; 2) simulated, severe spring drought followed by ambient precipitation thereafter plus summer irrigation (i.e., July and August); 3) ambient precipitation only; and 4) ambient precipitation plus summer irrigation. Results indicated substantial herbage production can be expected in this region during summer when precipitation is well above average because of the positive growth response of blue grama (Bouteloua gracilis [H.B.K.] Lag. ex Griffiths), the dominant warm-season grass growing in this region. However, results also showed that level of production in the study situation (i.e., spring drought, wet summer) was only about 50% of that attained in a normal (i.e., wet spring/dry summer) year. Moreover, long-term weather data shows the probability of receiving 23 normal precipitation in both July and August (i.e., our irrigation treatments) is < 1%. Thus, although these rangelands possess the capacity to respond favorably to summer precipitation, the low probability of receiving substantial levels of summer precipitation ensures levels of ecological and economic risk remain high.

Application of a rice growth and water balance model in an irrigated semi-arid subtropical environment

In order to enhance rice productivity in water-limited environments, there is a need to adopt water-saving practices. This study examines the applicability of the ORYZA2000 model in analyzing impact of water-related options on rice yield and water use for enhancing water productivity in irrigated environments of Punjab. Performance of the model was reasonably good as indicated by close matching between simulated harvest-time grain yield, biomass, and soil profile water use with measured data and trends of simulated water balance components for a range of soil and water management scenarios. Using long-term weather data, cumulative probability distribution of simulated grain yield and evapotranspiration (ET) showed that rice yield of late-transplanted (July 1) crop was comparable to that of early-transplanted (May 16) crop, and ET-based water productivity was greater in late-planted crop. Though ET-based water productivity with continuous submergence and 2-day interval regimes were comparable, water-input-based productivity was greater under 2-day interval regime. Increasing puddling intensity from P 1 (puddling once) to P 2 (puddling two times) and P 4 (puddling four times) regimes had little effect on ET-based water productivity, but caused a substantial increase in water-input-based productivity.

Characterization of Fate and Transport of Isoxaflutole, a Soil-Applied Corn Herbicide, in Surface Water Using a Watershed Model

The objective of this study is to conduct a comprehensive assessment of the transport of isoxaflutole and RPA 202248 and their accumulation potential in semistatic water bodies, using a distributed watershed scale model and observed water quality data. A conceptual model was developed to characterize the fate and transport of isoxaflutole residues to and in surface water. The soil and water assessment tool (SWAT), a continuous daily time-step watershed model, was used to simulate the processes identified in the conceptual model. Monitoring data were available for a number of surface water bodies within the major product use area as a result of extensive and intensive residue monitoring. Detailed product use information at the zip-code level was obtained through dealer sales and a grower survey. The hydrologic and chemical transport results from the SWAT model were validated by comparison to available monitoring data from selected water bodies. Upon validation, the model was used to simulate the fate of isoxaflutole-derived residues in the water bodies using long-term historical weather data. The results from this investigation indicate no evidence of long-term accumulation of isoxaflutole and its metabolite RPA 202248 in semistatic water bodies.

Empirical Models of Pollen Limitation, Resource Acquisition, and Mast Seeding by a Bee‐Pollinated Wildflower

Synchronous mast seeding is increasingly recognized as common in plant populations. Recent theoretical models show that synchronous mast seeding could be a consequence of resource allocation and storage within individual plants, coupled by pollen limitation in low-flowering years. We used long-term population and weather data to parameterize models of flowering based on stored resources and pollen limitation in Astragalus scaphoides, a bee-pollinated plant that flowers in alternate years. We used these models to test whether internal resource dynamics could explain mast seeding in A. scaphoides and, if so, whether synchrony was caused by pollen limitation and/or fluctuations in precipitation. We compared predictions of models that included all combinations of three factors: constant versus precipitation-dependent resource gain, uniform versus heterogeneous resource gain (among individual plants), and resource-dependent versus resource- and pollen-limited fruit set. Pollen limitation and heterogeneous resource gain were necessary and sufficient to explain alternate-year flowering, but precipitation increased the quantitative match between model predictions and flowering dynamics. Together, our results support the importance of density-dependent pollen limitation as an ultimate and proximate cause of mast seeding in A. scaphoides. Precipitation does not act as a direct cue for synchrony in this species but might affect long-term resource gain and fruiting dynamics.

Environmental risk analysis of farming systems in a semi-arid environment: effect of rotations and management practices on deep drainage

We investigated the impact of crop rotations and management practices on the water balance of farming systems in a semi-arid region of south-eastern Australia, where drainage beyond the root zone and rising water tables contribute to salinisation of soils and water streams. The CropSyst model was locally tested and used with long-term weather data to analyse the risk of deep drainage associated with alternative farming practices; water balances were estimated using both cascade and finite difference approaches. Model estimates were compared with (a) yield, biomass, phenology, soil water content and water use measured in a 5-year experiment involving two rotations, i.e. FWP (fallow-wheat–pea) and MWP (mustard–wheat–pea) and (b) yield measured in grower-managed wheat crops encompassing three seasons and broad ranges of weather, rotations, management practices and soils. In the rotation experiment, the simulations of yield were close to measured yield for wheat (observed range from ≈0 to 2.4 t ha −1; R 2 = 0.78; RMSE = 0.42 t ha −1), mustard (range from ≈0 to 0.6 t ha −1; R 2 = 0.57; RMSE = 0.20 t ha −1) and field pea (range from ≈0 to 1.4 t ha −1; R 2 = 0.72; RMSE = 0.29 t ha −1). Simulated wheat yield also compared well with on-farm measurements (observed range from ≈0 to 3.5 t ha −1; R 2 = 0.72, RMSE = 0.21 t ha −1). The model provided reasonable estimates of crop biomass, phenological development, soil water content and water use. Water use tended to be overestimated in some cases using the finite difference approach, but soil water content was well simulated in the presence of crop residues. The use of a finite-difference approach highlights the need to account for upward movement of water in the soil and its consequences on deep drainage. Median annual drainage simulated at 1.5 m was +9.1 mm for the fallow-wheat rotation, −5.4 mm for fallow-wheat–pea, −8.4 mm for mustard–wheat–pea, and +0.6 mm for wheat–wheat. The 3-year rotations (FWP and MWP) had the lowest median drainage and lowest probability of large drainage events in wet years. In the fallow-wheat rotation, median annual drainage was 24.8 mm with stubble retention and zero tillage, compared to −2.8 mm for stubble burnt and conventional tillage. Whilst the median response is indicative of comparative system performance, large episodic drainage events can be expected to be more frequent under fallow-wheat and even larger events with stubble retention and zero tillage. Stubble retention and zero tillage might be justified to increase and stabilise crop yield and reduce soil erosion but they do need to be assessed against the requirement to reduce deep drainage that is implicated in rising river and soil salinisation.

Evaluation of Weather Generator ClimGen for Southern Ontario

Modelling of soil water systems has become an important tool for water quality management and source water protection. Application of such models requires long-term continuous weather data from several weather stations distributed across the watershed. However, historic weather data often have missing records, large gaps in data or the length of the record may not be sufficient to include flood or drought conditions of interest. This has led to the development and use of weather generation procedures and tools. In this paper, the weather generator ClimGen has been evaluated for generation of daily precipitation, air temperature, solar radiation, wind speed, and relative humidity for southern Ontario conditions. The comparison of simulated weather data with 30 years of weather data for six stations indicated that ClimGen performed with reasonable accuracy with some limitations in generating rainfall intensities and solar radiation, particularly for the winter months.

Minimum data requirements for parameter estimation of stochastic weather generators

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 25:109-119 (2003) - doi:10.3354/cr025109 Minimum data requirements for parameter estimation of stochastic weather generators Afshin Soltani1,*, Gerrit Hoogenboom2 1Department of Agronomy and Plant Breeding, Gorgan University of Agricultural Sciences, Gorgan, Iran 2Department of Biological and Agricultural Engineering, The University of Georgia, Griffin, Georgia 30223, USA *Email: asoltani_99@yahoo.com ABSTRACT: Long-term daily weather data are commonly required for the application of simulation models in agricultural systems. The objective of this study was to evaluate the impact of the length of input weather data on the quality of generated weather data and to determine the minimum data required for an accurate estimation of the climate coefficients for a weather generator. Five locations in Iran with contrasting climates were selected for this study. The WGEN and SIMMETEO weather generators were parameterized using 5, 10, 15, 20 and 30 yr of weather data as the base period (BP), and then 30 yr daily weather data sets were generated corresponding to each BP. While WGEN requires daily weather data for parameter estimation, SIMMETEO uses monthly summaries. The quality of the generated weather was measured as percentage significant differences between observed and generated data. Statistical tests were conducted, including t- and F-tests, to compare differences between generated weather data versus 30 yr historical weather data. The results showed that the number of years required to obtain generated data that were similar to observed data was a function of the weather generator and the weather variable. WGEN was more sensitive to the amount of input data than SIMMETEO. In 8 of the 14 cases WGEN required a specific minimum amount of data to be efficient in weather generation versus 6 of the 14 cases for SIMMETEO. There was a correlation between generator performance and its minimum data requirement. For the cases where the generators showed a good performance, i.e. low-percentage significant differences between observed and generated data, a specific minimum number of years was required. For generating precipitation, a smaller number of years was required than for generating temperature. Based on the results from this study, it can be concluded that at least 15 yr of input data are needed for WGEN and SIMMETEO to obtain generated sequences with statistical characteristics similar to the observed data. However, a greater length of input data may lead to an improved set of generated data as shown through more stable generated data. KEY WORDS: Weather generation · Weather data · Climate · Simulation models · Decision support systems Full text in pdf format PreviousNextExport citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 25, No. 2. Online publication date: December 05, 2003 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2003 Inter-Research.

Influence of size of rainfall events on water-driven processes. II. Soil nitrogen mineralisation in a semi-arid environment

Power laws describe the relationships between the number N (s) and the size s of daily rainfall events, i.e. N (s) ~ s–τ, with higher τ corresponding to sites or seasons with greater frequency of small rainfall events. This paper tested the hypothesis that the rate of soil nitrogen mineralisation increases with increasing exponent τ, as affected by both spatial and temporal sources of variation. Rates of nitrogen mineralisation in an uncropped sandy loam soil were calculated using a simulation model with detailed nitrogen and water balances, and long-term weather data for 6 Australian locations in a range of annual rainfall from 260 to 360 mm. Daily rates of mineralisation were calculated using actual rainfall, and variable or fixed temperature and evaporative demand. The annual pattern of mineralisation rate, calculated as a function of rainfall and variable temperature and evaporative demand, was bimodal with peaks in April and November. These peaks disappeared and differences among locations were reduced when the effects of temperature and evaporative demand were removed. Under constant temperature and evaporative demand, mineralisation rates between April and November were 68% greater than rates between December and March. In the former period, characterised by a high frequency of small rainfall events, monthly mineralisation rate was a direct function of the amount of rainfall. In contrast, mineralisation was independent of the amount of rainfall during the period of larger, less frequent rainfall events from December to March. Parameter τ accounted for 75% of the variation in mineralisation rate in the period December–March and it also accounted for a substantial part of the variation between periods.

Model-based assessment of water balance components under different cropping systems in north-west India

Water balance components were assessed under main cropping systems of the Punjab state in north-west India with a simplified model using long-term weather data. Ten years’ average annual evapotranspiration (ET) was maximum in sugarcane (136 cm) followed by cotton–wheat (125 cm), rice–wheat (92 cm) and maize–wheat (86 cm) cropping system on a medium textured soil. However, on a coarse textured soil, ET was less by 6–11 cm under different cropping systems. Soil water evaporation ( E) constituted the major proportion of the annual water loss during establishment and senescence periods of the cropping cycles and intervening bare periods. Water loss by deep drainage in rice (68.2 cm) was more by 2.4 times than in maize, by 3.1 times than in cotton and by 3.3 times than in sugarcane. Irrigation water required to meet ET and drainage was maximum in rice–wheat (106.8 cm) followed by sugarcane (85.5 cm), cotton–wheat (81.6 cm) and maize–wheat system (61.2 cm). Straw mulching in maize, cotton and sugarcane decreased irrigation requirement further by reducing unproductive E losses. Thus, maize–wheat or cotton–wheat cropping systems having low irrigation requirement can be an appropriate alternative to rice–wheat system for maintaining balanced hydrology in the state.

Simulating phenology and yield response of canola to sowing date in Western Australia using the APSIM model

Canola is a relatively new crop in the Mediterranean environment of Western Australia and growers need information on crop management to maximise profitability. However, local information from field experiments is limited to a few seasons and its interpretation is hampered by seasonal rainfall variability. Under these circumstances, a simulation model can be a useful tool. The APSIM-Canola model was tested using data from Western Australian field experiments. These experiments included different locations, cultivars, and sowing dates. Flowering date was predicted by the model with a root mean squared deviation (RMSD) of 4.7 days. The reduction in the period from sowing to flowering with delay in sowing date was accurately reproduced by the model. Observed yields ranged from 0.1 to 3.2 t/ha and simulated yields from 0.4 to 3.0 t/ha. Yields were predicted with a RMSD of 0.3–0.4 t/ha. The yield reduction with delayed sowing date in the high, medium, and low rainfall region (3.2, 6.1, and 8.6% per week, respectively) was accurately simulated by the model (1.1, 6.7, and 10.3% per week, respectively). It is concluded that the APSIM-Canola model, together with long-term weather data, can be reliably used to quantify yield expectation for different cultivars, sowing dates, and locations in the grainbelt of Western Australia.

Interannual variability characteristics and simulated crop response of four stochastic weather generators

Many stochastic weather generators commonly used with crop models tend to under predict interannual variability of climate and, as a result, distort distributions of crop simulation results. We examine the ability of four stochastic weather generators, WeatherMan, MARKSIM, WM2 and LARS-WG, to reproduce interannual variability of monthly climate and crop simulation results. Comparisons were based on bias and RMSE of means and standard deviations of monthly precipitation totals, frequencies of wet days, mean daily temperature extremes for 12 long-term weather data sets; and yields and dates of anthesis and harvest maturity of three crops under a total of 10 scenarios simulated with dynamic crop models. Evaluation also considered statistical tests of equality of distributions of the same variables between observed and generated weather data sets. The generators generally reproduced climatic means well, except for MARKSIM which showed positive bias of mean monthly rainfall totals and frequencies. WM and LARS-WG showed substantial negative bias of interannual variability of monthly precipitation totals. As a result of stochastic resampling of wet-day frequencies, MARKSIM and WM2 showed little negative variability bias of monthly precipitation totals, but positive variability bias of monthly wet-day frequencies. WeatherMan, MARKSIM and LARS-WG under represented variability of monthly mean temperatures, whereas WM2 showed positive but smaller mean temperature variability bias. WM2 reproduced simulated distributions of yield and harvest maturity dates better than the other generators. Results for simulated anthesis dates were not consistent among generators. This study supports the need for some form of low-frequency variability correction in stochastic weather generators for applications in which reproducing interannual variability is important. Results generally favor WM2 over the other generators tested for applications in which variability of simulated yields is of primary importance.

Application of SP AW model parameters - optimum sowing date and water stress to explain wheat yield variability

The Soil-Plant-Atmosphere- Water (SPA W) model has been calibrated and validated using field experiment data from 1991-92 to 1993-94 for wheat crop at Varanasi district. Long-term (1973-74 to 1995-96) daily weather data were combined with general observation of wheat growth and soils to provide daily water budgets for 23 years. The model was calibrated with one year detailed crop growth characteristics and soil water observations and validated with another year soil water observations. The daily-integrated water stress index (WSI) values at the end of crop season correlated quite well with observed grain yield in this region.
 
 The water budget analysis shows a distinct optimum sowing period from 5th to 25th November and an optimum sowing date on 15th November with minimal water stress index. These results demonstrate the potential of SPA W model for planning irrigation scheduling and water management for wheat crop in this region.