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This paper focuses on the weed species Echinochloa crus-galli, commonly known as barnyardgrass, which is a persistent threat to crop yield and quality, especially in maize (Zea mays) cultivation. It is one of the most problematic weeds in agricultural fields due to its aggressive growth, adaptability to different environmental conditions and prolific seed production. The incidence of Echinochloa crus-galli in maize fields has increased in recent years. This study aims to provide a comprehensive understanding of the characteristics and behavior of Echinochloa crus-galli, and to suggest effective measures to control it. This research on sweetcorn was conducted from 1992 to 2019 at the Research and Education Center Gorzyń, Złotniki branch, which belongs to the Poznań University of Life Sciences. The evaluation of weed infestation was carried out in experiments focusing on chemical weed control in maize. The experiments were designed as a one-factor randomised block design with four field replications. The condition and the degree of weed infestation (number of weeds and fresh weight of weeds) in the control plots was assessed on an annual basis at the end of June and in July. The aim of the study was to evaluate the dynamic changes in the status and extent of barnyardgrass (Echinochloa crus-galli) infestation in maize grown after various other crops in the Wielkopolska region, with a focus on the weather conditions. The study found that barnyardgrass was most likely to occur when maize was sown after winter wheat in a dry and warm year, and least likely when maize was grown in rotation after winter wheat in a cold year with average rainfall. The proportion of barnyardgrass weed mass in the total weed mass was significantly lower after winter rye than after winter wheat, winter oilseed rape and winter triticale. Further research into the biology and ecology of barnyardgrass is key to effectively controlling this weed and safeguarding sweetcorn yields. [ABSTRACT FROM AUTHOR]

Nitrous oxide (N2O) emission estimation derived from the application of synthetic fertilizers using default emission factors and in a homogeneous way throughout the country for the Inventories of Greenhouse Gases of Argentina do not reflect local productive characteristics and the regional differences. The objectives of this study were a) to disaggregate synthetic nitrogen (N) data by region, crop and weather, b) to analyze and synthesize the state of the art of research in Argentina about the development of local N2O emission factors (FE) for synthetic fertilizers application. For the first objective, a methodology was developed that consists of multiplying the average N dose applied per region by the area sown per crop. For the second, a compilation of scientific papers published in national and international journals was carried out. It was possible to disaggregate the N application data by district and crop by crossing different sources of information: 36.7% of N at country level is applied in the Province of Buenos Aires, 26.3% in Córdoba, 12% in Santa Fe and 7.3% in Entre Ríos. Corn (Zea mays L.) and wheat (Triticum aestivum L.) are the crops with the highest application of N (33.9 and 23.3% respectively). Concerning the state of the art of local research, we found 11 papers that allow us to propose FE for direct N2O emissions, and 15 that allow us to estimate volatilization or leaching fractions that result in indirect N2O emissions. The average values estimated and reported from the literature were 0.0124 kg N2O-N (kg N)-1(direct N2O emission factors), 0.081 kg N volatilized (kg N)-1(volatilization fraction) and 0.244 kg N (kg N)-1 (leaching fraction). Compared to the values used in the National Inventory, the difference is up to 25%. [ABSTRACT FROM AUTHOR]

To clarify the current situation, hotspots, and development trends in the field of rice and climate change topic research, a massive literature dataset were analyzed from the Web of Science database by bibliometric method. The research theme was chosen given the continuous increase of studies related to climatic changes and their consequences to rice. Based on the Web of Science core database, this study analyzed 4170 papers in the field of rice and climate change topic research from 1990 to July 2022, which include 86 highly cited papers and 3 hot papers. Papers were mainly written in English (4157, 99.688%), from 16,363 authors, 4017 organizations, and 129 countries/territories, published in 841 journals and seven book series. The top five Journals are Science of the Total Environment (136, 3.261%), Sustainability (89, 2.134%), Agronomy Basel (81, 1.942%), Agricultural and Forest Meteorology (77, 1.847%), and Climatic Change (74, 1.775%), each published more than 74 papers. Top five countries and regions of People's Republic of China, the USA, India, Australia, and Japan were the major article contributors, each published more than 360 papers. Top five organizations of Chinese Acad Sci, Nanjing Agr Univ, Univ Chinese Acad Sci, Chinese Acad Agr Sci, and Int Rice Res Inst (IRRI) were popular based on contribution of articles more than 133 papers each. Among the all authors, top five authors were Tao Fulu, Pan Genxing, Zhang Zhao, Hasegawa Toshihiro, and Iizumi Toshichika, each published more than thirty papers. All keywords were separated into eight clusters for different research topics. Visualizations offer exploratory information on the current state in a scientific field or discipline as well as indicate possible developments in the future. The results will help researchers clarify the current situation in rice and climate change adaptation science but also provide guidance for future research. This work is also useful for student identifying graduate schools and researchers selecting journals. [ABSTRACT FROM AUTHOR]

Crop yield prediction gains growing importance for all stakeholders in agriculture. Since the growth and development of crops are fully connected with many weather factors, it is inevitable to incorporate meteorological information into yield prediction mechanism. The changes in climate-yield relationship are more pronounced at a local level than across relatively large regions. Hence, district or sub-region-level modeling may be an appropriate approach. To obtain a location- and crop-specific model, different models with different functional forms have to be explored. This systematic review aims to discuss research papers related to statistical and machine-learning models commonly used to predict crop yield using weather factors. It was found that Artificial Neural Network (ANN) and Multiple Linear Regression were the most applied models. Support Vector Regression (SVR) model has a high success ratio as it performed well in most of the cases. The optimization options in ANN and SVR models allow us to tune models to specific patterns of association between weather conditions of a location and crop yield. ANN model can be trained using different activation functions with optimized learning rate and number of hidden layer neurons. Similarly, the SVR model can be trained with different kernel functions and various combinations of hyperparameters. Penalized regression models namely, LASSO and Elastic Net are better alternatives to simple linear regression. The nonlinear machine learning models namely, SVR and ANN were found to perform better in most of the cases which indicates there exists a nonlinear complex association between crop yield and weather factors. [ABSTRACT FROM AUTHOR]

Agriculture is a highly weather-dependent activity, and climatic conditions impact both directly crop growth and indirectly diseases and pest developments causing yield losses. Weather forecasts are now a major component of various decision-support systems that assist farmers to optimize the positioning of crop protection treatments. However, properly accounting for weather uncertainty in these systems still remains a challenge. In this paper, three global and regional ensemble prediction systems (EPSs), covering different spatiotemporal scales, are coupled to a temperature-driven developmental model for grapevine moths in order to provide probabilistic forecasts of treatment dates. It is first shown that a parametric postprocessing of the EPSs significantly improves the prediction of treatment dates. Anticipating the need for phytosanitary treatments also requires seamless weather forecasts from the next hour to subseasonal time scales. An approach is presented to design seamless ensemble forecasts from the combination of the three EPSs used. The proposed method is able to leverage the increased performance of high-resolution EPS at short ranges, while ensuring a smooth transition toward larger-scale EPSs for longer ranges. The added value of this seamless integration on agronomic predictions is, however, difficult to assess with the current experimental setup. Additional simulations over a larger number of locations and years may be required. [ABSTRACT FROM AUTHOR]

A set of Python-based tools, WRF4PALM, has been developed for offline nesting of the PALM model system 6.0 into the Weather Research and Forecasting (WRF) modelling system. Time-dependent boundary conditions of the atmosphere are critical for accurate representation of microscale meteorological dynamics in high-resolution real-data simulations. WRF4PALM generates initial and boundary conditions from WRF outputs to provide time-varying meteorological forcing for PALM. The WRF model has been used across the atmospheric science community for a broad range of multidisciplinary applications. The PALM model system 6.0 is a turbulence-resolving large-eddy simulation model with an additional Reynolds-averaged Navier–Stokes (RANS) mode for atmospheric and oceanic boundary layer studies at microscale. Currently PALM has the capability to ingest output from the regional scale Consortium for Small-scale Modelling (COSMO) atmospheric prediction model. However, COSMO is not an open source model and requires a licence agreement for operational use or academic research (http://www.cosmo-model.org/ , last access: 23 April 2021). This paper describes and validates the new free and open-source WRF4PALM tools (available at https://github.com/dongqi-DQ/WRF4PALM , last access: 23 April 2021). Two case studies using WRF4PALM are presented for Christchurch, New Zealand, which demonstrate successful PALM simulations driven by meteorological forcing from WRF outputs. The WRF4PALM tools presented here can potentially be used for micro- and mesoscale studies worldwide, for example in boundary layer studies, air pollution dispersion modelling, wildfire emissions and spread, urban weather forecasting, and agricultural meteorology. [ABSTRACT FROM AUTHOR]

A set of Python-based tools, WRF4PALM, has been developed for offline-nesting of the PALM model system 6.0 into the Weather Research and Forecasting (WRF) modelling system. Time-dependent boundary conditions of the atmosphere are critical for accurate representation of microscale meteorological dynamics in high resolution real-data simulations. WRF4PALM generates initial and boundary conditions from WRF outputs to provide time-varying meteorological forcing for PALM. The WRF model has been used across the atmospheric science community for a broad range of multidisciplinary applications. The PALM model system 6.0 is a turbulence-resolving large-eddy simulation model with an additional Reynolds-averaged Navier–Stokes (RANS) mode for atmospheric and oceanic boundary layer studies at microscale (Maronga et al., 2020). Currently PALM has the capability to ingest output from the regional scale Consortium for Small-scale Modelling (COSMO) atmospheric prediction model. However, COSMO is not an open source model which requires a licence agreement for operational use or academic research (http://www.cosmo-model.org/). This paper describes and validates the new free and open-source WRF4PALM tools (available on https://github.com/dongqi-DQ/WRF4PALM). Two case studies using WRF4PALM are presented for Christchurch, New Zealand, which demonstrate successful PALM simulations driven by meteorological forcing from WRF outputs. The WRF4PALM tools presented here can potentially be used for micro- and mesoscale studies worldwide, for example in boundary layer studies, air pollution dispersion modelling, wildfire emissions and spread, urban weather forecasting, and agricultural meteorology [ABSTRACT FROM AUTHOR]

Somalia nation has been in the past classified as a volatile and of low economic value since 1990s when the government was deemed to have collapsed. Currently the independent Somalia is turning out to be one economic back bone of the Northern and Eastern parts of Africa in trade and agriculture. This paper focuses on the agricultural potential of Somalia using geospatial techniques through characterization. Climate is characterized and described, terrain models are derived, Soil analysis and hydrological assessment are also analyzed. Agroclimatic maps are developed to show suitable and unsuitable areas for agricultural production. The terrain of Somalia is more of a flat plain coupled with river basins that can be used as arable lands for agriculture. The ground water points can sustainably irrigate large number of parcels for crop production. Through GIS technology adopted for this particular work, two basins were mapped out to be possible agricultural potential areas: Juba and Shabelle. The assessment indicates that crop production can be achieved through irrigating of the arable plains and not relying so much on rain-fed agricultural systems. Agro-climatic zones of Somalia are delineated mainly due the amount of rainfall, temperature ranges and terrain patterns. With this study, farmers can be advised on which area to focus for crop production. The agricultural potential that lies in Somalia is so huge it could be named the future food basket of Africa, who knows? [ABSTRACT FROM AUTHOR]

Climate variability and change are recognised as a major threat for West African agriculture, particularly for smallholder farmers. Moreover, population pressure, poverty, and food insecurity, are worsening the vulnerability of production systems to climate risks. Application of Climate Services in agriculture, specifically Agrometeorological Services, is acknowledged as a valuable innovation to assist decision-making and develop farmers' specific adaptive capacities. In West Africa, the World Meteorological Organisation and National Meteorological Services deployed considerable efforts in the development of Agrometeorological Services. Nevertheless, the impacts of such services on West African farming communities are still largely unknown. This paper aims to delineate the added value of agrometeorological services for farmers within the Agriculture Innovation System of Mauritania. The results of this quali-quantitative assessment demonstrate that farmers use agrometeorological information for a variety of choices: making strategic choice on the seed variety and on the geographical distribution of plots, choosing the most appropriate planting date, better tuning crop development cycle with the rhythm of the rains and choosing favourable periods for different cultural operations. Globally, the effects of all these good practices can be summarized by an increase of crops productivity and a decrease of cropping costs (including opportunity cost) in terms of inputs and working time. [ABSTRACT FROM AUTHOR]

Extension agrometeorology is applied in agrometeorological extension work to advice and serve farmers. In agrometeorology, response farming has been developed decades ago. Climate change complicates response farming, but does not alter it. This paper reports on new operationalization of that response farming in new educational commitments in agroclimatology. It is explained how "Science Field Shops" are an example in Indonesia. This was based on a thorough analysis of what climate change means for farmers in Asia. For Africa, we report on eying the training of agrometeorological extension trainers ("product intermediaries") in West Africa, based on a thorough analysis of what climate change means for farmers in Africa. We also compare experience with reaching farmers in South Africa and farmer communities in Zambia, as new forms of supporting response farming, all under conditions of a changing climate. The paper, for the first time, connects results from four different programs the senior author is taking part in. There is first and foremost the need for training material to make it possible for the product intermediaries to participate in training extension intermediaries. This should, particularly, bring new knowledge to farmers. With what is presently available and with new approaches, climate extension should be developed and tested with farmers in ways that improve farmer preparedness and decision making. [ABSTRACT FROM AUTHOR]

A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data, which are eight-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season (end of DOY (EOD)) was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the LAD-logistic function parameters b1 and b2, which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (YP and YF) were established using LAD as the variable to predict corn yield. The yield model YP used LAD from predicted emergence to maturity, and the yield model YF used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the YP model, including the predicted phenology, performed much better than the YF model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The YP model showed similar or better performance, even for the much earlier pre-harvest yield prediction at DOY 257. In addition, the model performance showed no difference between the two study regions with very different climates and cultivation methods, including cultivar and irrigation management. These results suggested that the approach described in this paper has potential for application to relatively wide agroclimatic regions with different cultivation methods and for extension to the other crops. However, it needs to be examined further in tropical and sub-tropical regions, which are very different from the two study regions with respect to agroclimatic constraints and agrotechnologies. [ABSTRACT FROM AUTHOR]

In this paper, changes in the long and short spells of different rain intensities are statistically analyzed using daily gridded rainfall data prepared by the India Meteorological Department for the period 1951-2008. In order to study regional changes, analyses have been conducted over nine selected agro-meteorological (agro-met) divisions, five homogeneous zones, and also over the whole of India. Rain events of different intensities with continuous rainfall of more than or equal to 4 days are classified here as long spells. Those with less than 4 days are termed as short spells. Those results which are statistically significant at 95% confidence level are discussed in this paper. Trend analysis shows that during the summer monsoon months of June to September, short spell rain events with heavy intensity have increased over India as a whole. On the other hand, long spell rain events with moderate and low intensities have decreased in numbers. Results further show that the contributions of long spell moderate and short spell low-intensity rain events to the total rainfall have decreased whereas the contributions of short spell heavy and moderate-intensity rain events to the total seasonal rainfall have increased. Percentage changes in various categories of long and short spells in the decade 1991-2000 compared with the earlier decade 1951-1960, highlight the maximum increase in heavy-intensity short spell category and decrease in moderate-intensity long spell category in India as a whole and in most of the homogeneous zones and agro-met divisions. The changes in different types of rain events differ in the six homogeneous zones and nine selected agro-met divisions. However, in three homogeneous zones and three agro-met divisions, the short spell heavy-intensity rain events dominate as in the entire country. There are also changes observed in the monthly occurrences of above categories of rain events during the 4 months of summer monsoon. Such results with details of changes in rain categories in different parts of India have important implications in agriculture sector in the country. [ABSTRACT FROM AUTHOR]

This paper presents the methodology for assessment of drought episodes and their potential effects on winter and spring cereal crops in the Czech Republic (in the text referred to as Czechia). Historical climate and crop yields data for the period of 47 years (1961–2007) have been integrated into an agrometeorological database. The drought episodes were determined by three methods: according to the values of the standardized precipitation index (SPI), percentage of long-term precipitations ( r), and on the basis of the Ped drought index ( Si). Consequently, the combined SPI, Si, and r indices have been used as tools in identification of the severity, frequency, and extent of drought episodes. Additionally, the paper also presents the Si drought index and its potential use for real-time monitoring of spatial extension and severity of droughts in Czechia. The drought risk to crops was analyzed by identifying the relationships between the fluctuation of crop yields and drought index ( Si) based on the multiple regression analysis with stepwise selection. In general, models explain that a high percentage of the variability of the yield is due to drought (more than 45% of yield variance). [ABSTRACT FROM AUTHOR]

Historically, the olive grove has been one of the most emblematic ecosystems in Mediterranean countries. Currently, in Andalusia, Spain, the land under olive grove cultivation exceeds 1.5 million hectares, approximately 17% of the regional surface. Its exploitation has traditionally been based on the use of the available land and heterogeneous plantations, with different species adapted to southern Mediterranean climatic conditions, and to the management of the traditional olive cultivation culture. The objective of this work is to characterize the mechanical behavior of the atmospheric surface boundary layer (SBL) (under neutral stability) interacting with different olive grove configurations. Experimental tests were carried out in the Boundary Layer Wind Tunnel (BLWT) of the Andalusian Institute for Earth System Research (IISTA), University of Granada. Three representative configurations of olive groves under neutral atmospheric conditions were tested. The wind flow time series were recorded at several distances and heights downwind the olive plantation models with a cross hot wire anemometry system. Herein, this paper shows the airflow streamwise, including the mean flow and the turbulent characteristics. The spatial variability of these two mechanical magnitudes depends on, among others, the size, the agro-forest length, the layout of the tree rows, the porosity, the tree height, the crown shape and the surface vegetation cover. The aerodynamic diameter and Reynolds number for each agro-forest management unit are proposed as representative variables of the system response, as these could be related to olive grove management. The plantation, in turn, conforms to a windbreak, which affects the microclimate and benefits the elements of the ecosystem. Detailed knowledge of these variables and the interaction between the ecosystem and the atmosphere is relevant to optimize the resources management, land use and sustainability of the overall crop. Thus, this paper presents preliminary work to relate atmospheric variables to environmental variables, some of which could be humidity, erosion, evapotranspiration or pollen dispersion. [ABSTRACT FROM AUTHOR]

The impact of adverse weather conditions (AWCs) on crop production is random in both time and space and depends on factors such as severity, previous agrometeorological conditions, and plant vulnerability at a specific crop development stage. Any exclusion or improper treatment of any of these factors can cause crop models to produce significant under- or overestimates of yield. The analysis presented in this paper focuses on a range of agrometeorological indices (AMI) related to AWCs that might affect real yield as well as simulated yield. For this purpose, the analysis addressed four indicators of extreme temperatures and three indicators of dry conditions during the growth period of maize and winter wheat in Austria, Croatia, Serbia, Slovakia, and Sweden. It is shown that increases in the number and intensity of AWCs cannot be unambiguously associated with increased deviations in simulated yields. The identified correlations indicate an increase in modeling uncertainty. This finding represents important information for the crop modeling community. Additionally, it opens a window of opportunity for a statistical ("event scenario") approach based on correlations between agrometeorological indices of AWCs and crop yield data series. This approach can provide scenarios for certain locations, crop types, and AWC patterns and, therefore, improve yield forecasting in the presence of AWCs. [ABSTRACT FROM AUTHOR]

This paper consists of a principal component regression (PCR) model. This represents the yield of rice crop in Manipur's agro-climatic conditions. Primary data is used for the analysis. Findings show that the model fits the data well and diagnostic checks confirmed that data do not seem to contradict the general underlying assumptions about the model. Multiple correlation (R=0.881) suggest that out of the total variation 88.1% of variation in the yield is explained by the independent variables (principal components) used in the fitted model. [ABSTRACT FROM AUTHOR]

The variety of natural indicators, associated with weather forecasting and climate prediction, as used by farmers in the South-Western Free State province of South Africa, is described. Most farmers in this area were not familiar with the application of weather forecasts/climate predictions for agricultural production, or with other science-based agrometeorological products. They relied almost fully on their experience and traditional knowledge for farming decision making. The indicators for traditional knowledge are demonstrated here in broad terms, relying on the stories and indications from observations and years of experience of their use by the farmers. These means of engagement with the natural environment, are skills not well understood by most scientists, but useful to the farmers. They range from the constellation of stars, animal behavior, cloud cover and type, blossoming of certain indigenous trees, appearance and disappearance of reptiles, to migration of bird species and many others. It is suggested that some short-term traditional forecasts/predictions may be successfully merged with science-based climate predictions. The traditional knowledge and its use, reported on in this paper, is what scientists learned from farmers. Berkes was right that scholars have wasted too much time and effort on a science versus traditional knowledge debate; we should reframe it instead as a science and traditional knowledge dialogue and partnership. The complications of a changing climate make this even more necessary. [ABSTRACT FROM AUTHOR]

Official price of farmland in the Czech Republic is based on land value in different soil and climatic conditions. The paper compares relevant climatic and agroclimatic characteristics used for land appraisement. Characteristics defined in climatic region of estimated pedological ecological unit system for two fifty years period 1901-1950 and 1961-2010 were evaluated. Area of interest includes 53 points distributed within nine broad areas of the Czech Republic. It is evident that the development of climate has an enormous impact on soil fertility. Difference of station average values of air temperature of both fifty years vary from −0.5 to 1.1 ◦ C (mean difference is 0.3 ◦ C) in the case of vegetation period. The shift of precipitation is not so evident as in the case of temperature. The long term change in precipitation distribution within a year is documented by a different shift of annual, vegetation period and non-vegetation period values. Moisture certainty in vegetation period decreases in all cases of broad areas (except one region). All 50year averages of investigated parameters had been changed in 1961-2010 compared to the mean of 1901-1950. This should be taken into account when fixing the official price. Climatic region parameters should be replaced by a more complex 'agroclimatological characteristic', which take into account also the basic pedological and plant characteristics, for example the available water holding capacity. [ABSTRACT FROM AUTHOR]

Daily solar radiation at ground level is a necessary input variable required for the evaluation of evapotranspiration and crop growth, development, and yield-simulation models. Nevertheless, it is measured in few weather stations and at many locations it is not observed; also, available temporal series are generally no longer than a few years. A valid surrogate of measurement is the diurnal air-temperature range (); indeed, is inversely proportional to cloudiness and therefore could be a good indicator of atmospheric transmittance. As opposed to , daily maximum and minimum air temperatures are measured at many locations and their observations in developed countries began in the 19th century. For this reason, several models that permit indirect evaluation from air-temperature data have been suggested in the literature. The most famous models are the simple Hargreaves-Samani (HS) formula, many times recalibrated by the authors, and the Bristow-Campbell model, which has recently been improved. In this paper, the suitability of each proposed model is tested by comparing real data, recorded in 33 Sicilian agrometeorological stations in the period 2003-2008, typically representing the Mid-Mediterranean area, with estimates obtained by the models from data. In addition, a regional relationship is obtained for the scale coefficient K(T) of the Hargreaves-Samani formula: the relationship found improves data prediction with respect to the original HS formulation. [ABSTRACT FROM AUTHOR]

Grapevines are grown on a range of soils and in different climates. Depending on the desired final product and method of harvesting, the trellis system, and hence vineyard architecture, varies dramatically. Consequently, the crop coefficients used to estimate vineyard evapotranspiration from reference evapotranspiration are less universal than for other crops. Evapotranspiration measurements are often limited because of a lack of fetch and unavailability of vine parameters required as input. In this paper, a new procedure based on surface renewal (SR) analysis was derived to estimate sensible heat flux over the grapevines. A two-dimensional sonic anemometer operating close to the canopy can provide the inputs required to estimate the sensible heat flux and the work of expansion of the air parcels under constant pressure. Regardless of the stability conditions, the SR estimates were comparable to those determined using the eddy covariance (EC) method providing a root-mean-square error of 14 W m−2 for all data. Thus, the procedure is low cost and makes SR a simpler method than EC to estimate latent heat flux as the residual of the energy balance equation in moderately tall and heterogeneous crops. The experiment was carried out at a site influenced by regional advection of sensible heat flux. [ABSTRACT FROM AUTHOR]

The evaluation of biophysical models is usually carried out by estimating the agreement between measured and simulated data and, more rarely, by using indices for other aspects, like model complexity and overparameterization. In spite of the importance of model robustness, especially for large area applications, no proposals for its quantification are available. In this paper, we would like to open a discussion on this issue, proposing a first approach for a quantification of robustness based on the variability of model error to variability of explored conditions ratio. We used modelling efficiency (EF) for quantifying error in model predictions and a normalized agrometeorological index (SAM) based on cumulated rainfall and reference evapotranspiration to characterize the conditions of application. Population standard deviations of EF and SAM were used to quantify their variability. The indicator was tested for models estimating meteorological variables and crop state variables. The values provided by the robustness indicator (I R) were discussed according to the models’ features and to the typology and number of processes simulated. I R increased with the number of processes simulated and, within the same typology of model, with the degree of overparameterization. No correlation were found between I R and two of the most used indices of model error (RRMSE, EF). This supports its inclusion in integrated systems for model evaluation. [Copyright &y& Elsevier]

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

The distribution of cultivations in the world is restricted by the climatic limits, for defect or for excess of vital necessities for the individuals that conform the different biotypes. Since sow is made, plants are subjected to asynchronous variations of component elements of climate and it is the climate the main decisive factor of probability of success of cultivation. The objective of this paper was the delimitation of the Argentinean geographical area where it is possible the economic production of Jatropha curcas, Ricinus communis and Cynara cardúnculus without watering. Since extracted data of the Climatic Statistics published by the National Meteorological Service it was sketched the climatic indexes that define the agroclimatic zone of the specie for the period 1961-1990 for meteorological and agrometeorological stations distributed in the country. Then, all maps corresponding to each one of the climatic indexes were elaborated and the resulting map of the overlapping of all previous ones defined the agroclimatic zone of Jatropha curcas (physic nut, piñón manso, tempate), Ricinus communis (tártago, ricino) and Cynara cardunculus (cardo). [ABSTRACT FROM AUTHOR]

Wheat yield variability will increase in the future due to the projected increase in extreme weather events and long-term climate change effects. Currently, regional agricultural statistics are used to monitor wheat yield. Remotely sensed vegetation indices have a higher spatio-temporal resolution and could give more insight into crop yield. In this paper, we (i) evaluate the possibility to use Normalized Difference Vegetation Index (NDVI) time series to estimate wheat yield in Latvia and (ii) determine which weather variables impact wheat yield changes using both ALARO-0 and REMO Regional Climate Models (RCM) output. The integral from NDVI series (aNDVI) for winter and spring wheat fields is used as a predictor to model regional wheat yield from 2014 to 2018. A correlation analysis between weather variables, wheat yield and aNDVI was used to elucidate which weather variables impact wheat yield changes in Latvia. Our results indicate that high temperatures in June for spring wheat and in July for winter wheat had a negative correlation with yield. A linear regression yield model explained 71% of the variability with a residual standard error of 0.55 Mg/ha. When RCM data were added as predictor variables to the wheat yield empirical model a random forest approach resulted in better results compared to a linear regression approach, the explained variance increased up to 97% and the residual standard error decreased to 0.17 Mg/ha. We conclude that NDVI time series and RCM output enabled regional crop yield and weather impact monitoring at higher spatio-temporal resolutions than regional statistics. [ABSTRACT FROM AUTHOR]

The analysis and interpretation of data on the agro-climatic resources of a cultivated area offer the possibility to characterize the degree of favorability for cultivated species depending on the climate and soil bid. Depending on the climate, soil and the agricultural species requirements, the agro-technical management system must be applied properly, so as to allow the improvement of agricultural productivity. The aim of this study is to assess the agroclimatic resources of the Oltenia region, for the period 1961-2008 and especially of the Olt County, in terms of thermic and precipitation resources for the period 1990-2013. The knowing of these conditions is required for choosing the cultivated product and the crop production interventions used for obtaining optimal economic crops. [ABSTRACT FROM AUTHOR]

As knowing the impact of weather phenomena and their effects on agriculture represents a primary necessity, at present the importance of agro-meteorological information for economy, in general, and for the agriculture sector, in particular, involves a complex approach of the relation environment--plant. The agro-climatologic resource's assessment is multi-way needed: to choose the product and to cultivate varieties; for different phyto-technical interventions used to improve soil fertility; it is also economically fundamental in order to obtain optimum yields. This article frames an analysis and a synthesis of the main agro-climatic parameters, the air temperature and the precipitation for the region of Muntenia, considering the period between 1961- 2009. As this region has the largest agricultural land in Romania, is it useful and necessary for everyone who aims to carry on agricultural business or studies to be well informed on the agro-climatologic potential that characterizes it. [ABSTRACT FROM AUTHOR]