Toward a combined seasonal weather and crop productivity forecasting system: Determination of the working spatial scale

A methodology is presented for the development of a combined seasonal weather and crop productivity forecasting system. The first stage of the methodology is the determination of the spatial scale(s) on which the system could operate; this determination has been made for the case of groundnut production in India. Rainfall is a dominant climatic determinant of groundnut yield in India. The relationship between yield and rainfall has been explored using data from 1966 to 1995. On the all-India scale, seasonal rainfall explains 52% of the variance in yield. On the subdivisional scale, correlations vary between variance r(2) = 0.62 (significance level p < 10(-4)) and a negative correlation with r(2) = 0.1 (p = 0.13). The spatial structure of the relationship between rainfall and groundnut yield has been explored using empirical orthogonal function (EOF) analysis. A coherent, large-scale pattern emerges for both rainfall and yield. On the subdivisional scale (similar to 300 km), the first principal component (PC) of rainfall is correlated well with the first PC of yield (r(2) = 0.53, p < 10(-4)), demonstrating that the large-scale patterns picked out by the EOFs are related. The physical significance of this result is demonstrated. Use of larger averaging areas for the EOF analysis resulted in lower and (over time) less robust correlations. Because of this loss of detail when using larger spatial scales, the subdivisional scale is suggested as an upper limit on the spatial scale for the proposed forecasting system. Further, district-level EOFs of the yield data demonstrate the validity of upscaling these data to the subdivisional scale. Similar patterns have been produced using data on both of these scales, and the first PCs are very highly correlated (r(2) = 0.96). Hence, a working spatial scale has been identified, typical of that used in seasonal weather forecasting, that can form the basis of crop modeling work for the case of groundnut production in India. Last, the change in correlation between yield and seasonal rainfall during the study period has been examined using seasonal totals and monthly EOFs. A further link between yield and subseasonal variability is demonstrated via analysis of dynamical data.

Physical quality of a yellow latossol under integrated crop-livestock system

Soil physical quality is essential to global sustainability of agroecosystems, once it is related to processes that are essential to agricultural crop development. This study aimed to evaluate physical attributes of a Yellow Latossol under different management systems in the savanna area in the state of Piaui. This study was developed in Uruçuí southwest of the state of Piauí. Three systems of soil management were studied: an area under conventional tillage (CT) with disk plowi and heavy harrow and soybean crop; an area under no-tillage with soybean-maize rotation and millet as cover crop (NT + M); two areas under Integrated Crop-Livestock System, with five-month pasture grazing and soybean cultivation and then continuous pasture grazing (ICL + S and ICL + P, respectively). Also, an area under Native Forest (NF) was studied. The soil depths studied were 0.00-0.05, 0.05-0.10 and 0.10-0.20 m. Soil bulk density, as well as porosity and stability of soil aggregates were analyzed as physical attributes. Anthropic action has changed the soil physical attributes, in depth, in most systems studied, in comparison to NF. In the 0.00 to 0.05 m depth, ICL + P showed higher soil bulk density value. As to macroporosity, there was no difference between the management systems studied and NF. The management systems studied changed the soil structure, having, as a result, a small proportion of soil in great aggregate classes (MWD). Converting native forest into agricultural production systems changes the soil physical quality. The Integrated Crop-Livestock System did not promote the improvement in soil physical quality.

Straw decomposition of nitrogen-fertilized grasses intercropped with irrigated maize in an integrated crop-livestock system

The greatest limitation to the sustainability of no-till systems in Cerrado environments is the low quantity and rapid decomposition of straw left on the soil surface between fall and spring, due to water deficit and high temperatures. In the 2008/2009 growing season, in an area under center pivot irrigation in Selvíria, State of Mato Grosso do Sul, Brazil, this study evaluated the lignin/total N ratio of grass dry matter , and N, P and K deposition on the soil surface and decomposition of straw of Panicum maximum cv. Tanzânia, P. maximum cv. Mombaça, Brachiaria. brizantha cv. Marandu and B. ruziziensis, and the influence of N fertilization in winter/spring grown intercropped with maize, on a dystroferric Red Latosol (Oxisol). The experiment was arranged in a randomized block design in split-plots; the plots were represented by eight maize intercropping systems with grasses (sown together with maize or at the time of N side dressing). Subplots consisted of N rates (0, 200, 400 and 800 kg ha-1 year-1) sidedressed as urea (rates split in four applications at harvests in winter/spring), as well as evaluation of the straw decomposition time by the litter bag method (15, 30, 60, 90, 120, and 180 days after straw chopping). Nitrogen fertilization in winter/spring of P. maximum cv. Tanzânia, P. maximum cv. Mombaça, B. brizantha cv. Marandu and B. ruziziensis after intercropping with irrigated maize in an integrated crop-livestock system under no-tillage proved to be a technically feasible alternative to increase the input of straw and N, P and K left on the soil surface, required for the sustainability of the system, since the low lignin/N ratio of straw combined with high temperatures accelerated straw decomposition, reaching approximately 30 % of the initial amount, 90 days after straw chopping.

MICROBIAL CHARACTERISTICS OF SOILS UNDER AN INTEGRATED CROP-LIVESTOCK SYSTEM

Integrated crop-livestock systems (ICLs) are a viable strategy for the recovery and maintenance of soil characteristics. In the present study, an ICL experiment was conducted by the Instituto Agronômico do Paraná in the municipality of Xambre, Parana (PR), Brazil, to evaluate the effects of various grazing intensities. The objective of the present study was to quantify the levels of microbial biomass carbon (MBC) and soil enzymatic activity in an ICL of soybean (summer) and Brachiaria ruziziensis (winter), with B. ruziziensis subjected to various grazing intensities. Treatments consisted of varying pasture heights and grazing intensities (GI): 10, 20, 30, and 40 cm (GI-10, GI-20, GI-30, and GI-40, respectively) and a no grazing (NG) control. The microbial characteristics analysed were MBC, microbial respiration (MR), metabolic quotient (qCO2), the activities of acid phosphatase, β-glucosidase, arylsuphatase, and cellulase, and fluorescein diacetate (FDA) hydrolysis. Following the second grazing cycle, the GI-20 treatment (20-cm - moderate) grazing intensity) contained the highest MBC concentrations and lowest qCO2 concentrations. Following the second soybean cycle, the treatment with the highest grazing intensity (GI-10) contained the lowest MBC concentration. Soil MBC concentrations in the pasture were favoured by the introduction of animals to the system. High grazing intensity (10-cm pasture height) during the pasture cycle may cause a decrease in soil MBC and have a negative effect on the microbial biomass during the succeeding crop. Of all the enzymes analyzed, only arylsuphatase and cellulase activities were altered by ICL management, with differences between the moderate grazing intensity (GI-20) and no grazing (NG) treatments.

Pilot Project for a Hybrid Road-Flooding Forecasting System on Squaw Creek, TR-642

A network of 25 sonic stage sensors were deployed in the Squaw Creek basin upstream from Ames Iowa to determine if the state-of-the-art distributed hydrological model CUENCAS can produce reliable information for all road crossings including those that cross small creeks draining basins as small as 1 sq. mile. A hydraulic model was implemented for the major tributaries of the Squaw Creek where IFC sonic instruments were deployed and it was coupled to CUENCAS to validate the predictions made at small tributaries in the basin. This study demonstrates that the predictions made by the hydrological model at internal locations in the basins are as accurate as the predictions made at the outlet of the basin. Final rating curves based on surveyed cross sections were developed for the 22 IFC-bridge sites that are currently operating, and routine forecast is provided at those locations (see IFIS). Rating curves were developed for 60 additional bridge locations in the basin, however, we do not use those rating curves for routine forecast because the lack of accuracy of LiDAR derived cross sections is not optimal. The results of our work form the basis for two papers that have been submitted for publication to the Journal of Hydrological Engineering. Peer review of our work will gives a strong footing to our ability to expand our results from the pilot Squaw Creek basin to all basins in Iowa.

Soil salinization and maize and cowpea yield in the crop rotation system using saline waters

The use of saline water and the reuse of drainage water for irrigation depend on long-term strategies that ensure the sustainability of socio-economic and environmental impacts of agricultural systems. In this study, it was evaluated the effects of irrigation with saline water in the dry season and fresh water in the rainy season on the soil salt accumulation yield of maize and cowpea, in a crop rotation system. The experiment was conducted in the field, using a randomized complete block design, with five replications. The first crop was installed during the dry season of 2007, with maize irrigated with water of different salinities (0.8, 2.2, 3.6 and 5.0 dS m-1). The maize plants were harvested at 90 days after sowing (DAS), and vegetative growth, dry mass of 1000 seeds and grain yield were evaluated. The same plots were utilized for the cultivation of cowpea, during the rainy season of 2008. At the end of the crop, cycle plants of this species were harvested, being evaluated the vegetative growth and plant yield. Soil samples were collected before and after maize and cowpea cultivation. The salinity of irrigation water above 2.2 dS m-1 reduced the yield of maize during the dry season. The high total rainfall during the rainy season resulted in leaching of salts accumulated during cultivation in the dry season, and eliminated the possible negative effects of salinity on cowpea plants. However, this crop showed atypical behavior with a significant proportion of vegetative mass and low pod production, which reduced the efficiency of this strategy of crop rotation under the conditions of this study.

Toward an integrated seasonal forecasting system for South America

This study proposes an objective integrated seasonal forecasting system for producing well-calibrated probabilistic rainfall forecasts for South America. The proposed system has two components: ( i) an empirical model that uses Pacific and Atlantic sea surface temperature anomalies as predictors for rainfall and ( ii) a multimodel system composed of three European coupled ocean - atmosphere models. Three-month lead austral summer rainfall predictions produced by the components of the system are integrated ( i. e., combined and calibrated) using a Bayesian forecast assimilation procedure. The skill of empirical, coupled multimodel, and integrated forecasts obtained with forecast assimilation is assessed and compared. The simple coupled multimodel ensemble has a comparable level of skill to that obtained using a simplified empirical approach. As for most regions of the globe, seasonal forecast skill for South America is low. However, when empirical and coupled multimodel predictions are combined and calibrated using forecast assimilation, more skillful integrated forecasts are obtained than with either empirical or coupled multimodel predictions alone. Both the reliability and resolution of the forecasts have been improved by forecast assimilation in several regions of South America. The Tropics and the area of southern Brazil, Uruguay, Paraguay, and northern Argentina have been found to be the two most predictable regions of South America during the austral summer. Skillful rainfall forecasts are generally only possible during El Nino or La Nina years rather than in neutral years.

Predictability and skill of boreal winter forecasts made with the ECMWF Seasonal Forecasting System II

The performance of boreal winter forecasts made with the European Centre for Medium-Range Weather Forecasts (ECMWF) System 11 Seasonal Forecasting System is investigated through analyses of ensemble hindcasts for the period 1987-2001. The predictability, or signal-to-noise ratio, associated with the forecasts, and the forecast skill are examined. On average, forecasts of 500 hPa geopotential height (GPH) have skill in most of the Tropics and in a few regions of the extratropics. There is broad, but not perfect, agreement between regions of high predictability and regions of high skill. However, model errors are also identified, in particular regions where the forecast ensemble spread appears too small. For individual winters the information provided by t-values, a simple measure of the forecast signal-to-noise ratio, is investigated. For 2 m surface air temperature (T2m), highest t-values are found in the Tropics but there is considerable interannual variability, and in the tropical Atlantic and Indian basins this variability is not directly tied to the El Nino Southern Oscillation. For GPH there is also large interannual variability in t-values, but these variations cannot easily be predicted from the strength of the tropical sea-surface-temperature anomalies. It is argued that the t-values for 500 hPa GPH can give valuable insight into the oceanic forcing of the atmosphere that generates predictable signals in the model. Consequently, t-values may be a useful tool for understanding, at a mechanistic level, forecast successes and failures. Lastly, the extent to which t-values are useful as a predictor of forecast skill is investigated. For T2m, t-values provide a useful predictor of forecast skill in both the Tropics and extratropics. Except in the equatorial east Pacific, most of the information in t-values is associated with interannual variability of the ensemble-mean forecast rather than interannual variability of the ensemble spread. For GPH, however, t-values provide a useful predictor of forecast skill only in the tropical Pacific region.

Design of a forecasting system for ECMWF

As laid out in its convention there are 8 different objectives for ECMWF. One of the major objectives will consist of the preparation, on a regular basis, of the data necessary for the preparation of medium-range weather forecasts. The interpretation of this item is that the Centre will make forecasts once a day for a prediction period of up to 10 days. It is also evident that the Centre should not carry out any real weather forecasting but merely disseminate to the member countries the basic forecasting parameters with an appropriate resolution in space and time. It follows from this that the forecasting system at the Centre must from the operational point of view be functionally integrated with the Weather Services of the Member Countries. The operational interface between ECMWF and the Member Countries must be properly specified in order to get a reasonable flexibility for both systems. The problem of making numerical atmospheric predictions for periods beyond 4-5 days differs substantially from 2-3 days forecasting. From the physical point we can define a medium range forecast as a forecast where the initial disturbances have lost their individual structure. However we are still interested to predict the atmosphere in a similar way as in short range forecasting which means that the model must be able to predict the dissipation and decay of the initial phenomena and the creation of new ones. With this definition, medium range forecasting is indeed very difficult and generally regarded as more difficult than extended forecasts, where we usually only predict time and space mean values. The predictability of atmospheric flow has been extensively studied during the last years in theoretical investigations and by numerical experiments. As has been discussed elsewhere in this publication (see pp 338 and 431) a 10-day forecast is apparently on the fringe of predictability.

Technical Note: The normal quantile transformation and its application in a flood forecasting system.

The Normal Quantile Transform (NQT) has been used in many hydrological and meteorological applications in order to make the Cumulated Distribution Function (CDF) of the observed, simulated and forecast river discharge, water level or precipitation data Gaussian. It is also the heart of the meta-Gaussian model for assessing the total predictive uncertainty of the Hydrological Uncertainty Processor (HUP) developed by Krzysztofowicz. In the field of geo-statistics this transformation is better known as the Normal-Score Transform. In this paper some possible problems caused by small sample sizes when applying the NQT in flood forecasting systems will be discussed and a novel way to solve the problem will be outlined by combining extreme value analysis and non-parametric regression methods. The method will be illustrated by examples of hydrological stream-flow forecasts.

An easily implemented agro-hydrological procedure with dynamic root simulation for water transfer in the crop-soil system: validation and application

Models for water transfer in the crop-soil system are key components of agro-hydrological models for irrigation, fertilizer and pesticide practices. Many of the hydrological models for water transfer in the crop-soil system are either too approximate due to oversimplified algorithms or employ complex numerical schemes. In this paper we developed a simple and sufficiently accurate algorithm which can be easily adopted in agro-hydrological models for the simulation of water dynamics. We used a dual crop coefficient approach proposed by the FAO for estimating potential evaporation and transpiration, and a dynamic model for calculating relative root length distribution on a daily basis. In a small time step of 0.001 d, we implemented algorithms separately for actual evaporation, root water uptake and soil water content redistribution by decoupling these processes. The Richards equation describing soil water movement was solved using an integration strategy over the soil layers instead of complex numerical schemes. This drastically simplified the procedures of modeling soil water and led to much shorter computer codes. The validity of the proposed model was tested against data from field experiments on two contrasting soils cropped with wheat. Good agreement was achieved between measurement and simulation of soil water content in various depths collected at intervals during crop growth. This indicates that the model is satisfactory in simulating water transfer in the crop-soil system, and therefore can reliably be adopted in agro-hydrological models. Finally we demonstrated how the developed model could be used to study the effect of changes in the environment such as lowering the groundwater table caused by the construction of a motorway on crop transpiration. (c) 2009 Elsevier B.V. All rights reserved.

Evaluation of the Plant–Craig stochastic convection scheme (v2.0) in the ensemble forecasting system MOGREPS-R (24 km) based on the Unified Model (v7.3)

The Plant–Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic scheme only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant–Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant–Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.