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Based on the Essential Science Indicators (ESI) database, this study analyzed 734 top papers in the subject category of Agronomy from 2009 to 2019, which include 730 highly cited papers and seven hot papers in the field. All papers written in English, were from 3,540 authors; 1,225 organizations; and 91 countries/territories, published in 47 journals and book series in the field of Agronomy. The journals with the highest impact factor (IF) were Industrial Crops and Products, Plant and Soil, Agricultural and Forest Meteorology, Pest Management Science, and Field Crops Research. The countries and regions with the highest IF were United States, People's Republic of China, France, Germany, and Australia. The organizations with the highest average IF were INRA, University of Western Australia, Chinese Academy of Science, China Agriculture University, and University of California-Davis. This work is useful for students identifying graduate schools and researchers selecting journals. [ABSTRACT FROM AUTHOR]

This study analyzed 302 top papers in the subject category of Forestry from 2010 to 2020, which include 300 highly cited papers and 3 hot papers. All papers were written in English, belonging to 1,567 authors, 694 organizations and 74 countries/territories, published in 35 journals and book series. The top five Journals were Agricultural and Forest Meteorology, Forest Ecology and Management, Forests, Tree Physiology and Forest Policy and Economics. Top five countries and regions were USA, Germany, Canada, France and Peoples R China. Top five organizations were US Forest Serv, Chinese Acad Sci, INRA, Swedish Univ Agr Sci and Univ Freiburg. Among the all authors, Bauhus, Juergen from Univ Freiburg of Germany provided the highest number in 6 papers. All keywords were separated into seven clusters for different research topics. Visualizations offered exploratory information on the current state in a scientific field or discipline as well as indicated possible developments in the future. [ABSTRACT FROM AUTHOR]

A quantitative analysis of scientific articles published in the Journal of Agrometeorology (JAM) between 2008 and 2022 was conducted using SCOPUS database and a variety of scientometric indicators. Various metrics were utilized to examine aspects including yearly research output, highly referenced sources, author rankings, contributions and profiles, cooperation trends, highly contributing nations, most cited papers, commonly searched keywords and worldwide collaboration mapping. This study employs biblioshiny for analysis and only looks at data that is available in Scopus database. With an h-index (17), a g-index (21) and 3238 total citations across the study period, the journal demonstrated considerable influence. With the greatest number of research publications (n=46) and the greatest number of citations (236), Pandey V stands out among other authors. In terms of the number of papers and citations, India emerged as the leading nation, with the Punjab Agricultural University in the lead with 744 publications. Four clusters were found by co-citation network analysis, with Allen RG being the most quoted author among them. The study also highlighted the fact that Indian authors worked together the most. This analysis is important for assessing the influence of the JAM and offers insightful information about noteworthy research trends and developments in the scientific community. [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]

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]

This paper explores the under-appreciated role of evolving temperature variability in crop production in the overall relationship between crop production and climate change. Prior studies can be characterized by two broad approaches: estimation of the impact of climate change over some observed period and forecasting crop production levels under feasible climate scenarios. A standard method for predicting crop production under climate change involves regressing logged crop production on quadratic weather variables and region-level fixed effects (Lobell and Burke, I Agricultural and Forest Meteorology i , 2010). [Extracted from the article]

The Journal of Agricultural Meteorology is an international peer-reviewed open access journal published by the Society of Agricultural Meteorology of Japan. The journal aims to improve understanding of biophysical and biochemical processes in agriculture, forestry, and natural ecosystems at various scales. It covers a wide range of topics including weather disasters, climate change, soil environments, plant response to environmental change, remote sensing, and adaptation and mitigation strategies. The journal has been publishing important original papers and review articles for 80 years and has recently started a program to enhance its international dissemination and editing management. This collection of articles is the first compilation by the managing editor and showcases the latest research activities in the field. [Extracted from the article]

China is the largest soybean-consuming country in the world, but its self-sufficiency rate (SSR) of 16% is very low and it therefore has to heavily rely on imports. To solve the soybean dilemma in China, it is necessary to examine the maximum amount of soybean that could be grown on the land currently used, how much land could reasonably be used to expand soybean acreage, and whether China could sustainably increase soybean self-sufficiency to reduce the risks of import reliance. To answer these questions, our paper presents a high-resolution spatial analysis of potential soybean production in China using primary data of weather and crop production practices that govern this potential. We employed a "bottom-up" scaling protocol to estimate gaps between potential yield with optimal management and current yields in three major soybean-planting regions, namely, Northeast China, Central China, and South China. We found that current soybean yield gap (Yg) in China is 49% and 45% of potential yield under irrigated and rainfed cropping systems, respectively. By closing the yield gap, Northeast China could provide additional soybean production equivalent to 32% of the current national total. Our results show that SSR could only be increased to 21–23% in 2030 by Yg closure alone but could be increased to a maximum of 52% by combining Yg closure and a reasonable area expansion. Even so, at least 61.08 million tons of soybean accounting for 38% of global soybean trade would still need to be imported to meet future domestic demand. We discuss strategies for soybean production increase based on Yg closure in the most valuable areas and cropland expansion in a sustainable manner in order to increase SSR as well as lessen the import pressure on the global market. [ABSTRACT FROM AUTHOR]

This paper discusses the advance in our understanding of the interaction between wind and trees over the last 25 years. It does this by comparing papers in this special issue of Forestry and a companion volume in Agricultural and Forest Meteorology, based on work presented at the 8th IUFRO Wind and Trees Conference in 2017, with papers published in book format after the 1st IUFRO Wind and Trees Conference in 1993. The analysis is divided into sections on 'airflow and tree mechanics', 'forest management and ecology', 'tree adaptation and acclimation' and 'modelling and statistical techniques'. It is clear that in the last 25 years large advances have been made in our understanding of airflow and turbulence within and above forest canopies and the wind flow and wind loading around and on individual trees. There have also been important developments in the mechanistic and statistical modelling of wind damage risk to forests. Much of the progress has been aided by the enormous advances in measurement systems, computer power and modelling techniques. Furthermore, this knowledge is beginning to influence the approach to temperate and boreal forest management and the development of systems to mitigate the risk of wind damage. At the same time there has been a growing awareness of the ecological impact of wind in many forests around the world, including tropical forests. However, other areas of the effect of wind on trees have progressed much less in the last 25 years. This includes the process of fatiguing in the root-soil system, both during individual storms and over longer periods, and which is known to be a critical factor in the process of windthrow. In addition the exact nature of damage propagation within forests during a storm, and then in subsequent storms, has received relatively little attention although new studies using advanced computational methods are making advances. Of particular relevance, and despite the known critical importance of tree acclimation to the wind, there have been very few studies in the last 25 years on wind acclimation in full-size trees. This is an area of enormous importance in understanding how resistance to the wind varies for different species and between areas with different wind climates. In summary, overall much has been learnt since the 1st Wind and Trees conference but there remain many intriguing and exciting challenges ahead for this multi-disciplinary subject. [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]

This paper analyzes the effects of climatic variables on food crop yields at the prefectural level from 1996 to 2016 in Togo. Using the panel‐corrected standard errors method and panel data from departments in charge of agricultural statistics and meteorology, the results show that meteorological variables have various effects on food crops yields, but the negative effects are dominant. In addition, adaptation strategies through agricultural land reallocation have not enabled farmers to improve food crops yields. There is an urgent need for public authorities to implement actions to strengthen farmers' resilience through practice adoption and cultural innovations for adaptation to climate change. [ABSTRACT FROM AUTHOR]

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Background. This paper analyzes the impact of changes in fertilization on crop yields and the runoff of nutrients from a small agricultural catchment (176 km²) to a shallow bay, using the SWAT model. Puck Bay is part of the Gulf of Gdansk and belongs to the Baltic Sea. The whole area of Puck Bay (364 km2) is protected (Natura 2000) yet despite this it suffers from eutrophication problems due to the relatively minimal depth and difficult water exchange. Methods. The paper presents a comparison of the calculated yields and the runoff of nutrients and pesticides in the SWAT model, for a small agricultural coastal catchment. Calculations were made for 13 crop scenarios with weather data from 2011 to 2019. For each crop, an agriculture calendar was made. Two variants of fertilization were considered (autofertilization mode and according to the calendar). The nutrient runoff was calculated depending on the adopted scenario. In addition, the fate of selected pesticides was simulated. Results. Depending on the crop, the annual load of NO3into the stream ranged from 0.74 to 3.65 kg ha-1. The annual load of organic phosphorous into the stream was between 0.686 and 3.64 kg ha-1. This is lower than in the majority of EU or Baltic countries. The surface runoff of dissolved Glyphosate was equal to 286 mg ha-1. The annual loads of nutrients from the catchment area are equivalent in both fertilization modes. Regardless of the selected fertilization mode, in addition to the dosage, the form of nutrients is important for the model. [ABSTRACT FROM AUTHOR]

A quantitative analysis of scientific articles published in the Journal of Agrometeorology (JAM) between 2008 and 2022 was conducted using a variety of scientometric indicators. Various metrics were utilized to examine aspects including yearly research output, highly referenced sources, author rankings, contributions and profiles, cooperation trends, highly contributing nations, most cited papers, commonly searched keywords and worldwide collaboration mapping. This study employs biblioshiny for analysis and only looks at data that is available in Scopus database. With an h-index (17), a g-index (21) and 3238 total citations across the study period, the journal demonstrated considerable influence. With the greatest number of research publications (n=46) and the greatest number of citations (236), Pandey V stands out among other authors. In terms of the number of papers and citations, India emerged as the leading nation, with the Punjab Agricultural University in the lead with 744 publications. Four clusters were found by co-citation network analysis, with Allen RG being the most quoted author among them. The study also highlighted the fact that Indian authors worked together the most. This analysis is important for assessing the influence of the JAM and offers insightful information about noteworthy research trends and developments in the scientific community.

Coffee plays a key role in the generation of rural employment in Colombia. More than 785,000 workers are directly employed in this activity, which represents the 26% of all jobs in the agricultural sector. Colombian coffee growers estimate the production of cherry coffee with the main aim of planning the required activities, and resources (number of workers, required infrastructures), anticipating negotiations, estimating, price, and foreseeing losses of coffee production in a specific territory. These important processes can be affected by several factors that are not easy to predict (e.g., weather variability, diseases, or plagues.). In this paper, we propose a non-destructive time series model, based on weather and crop management information, that estimate coffee production allowing coffee growers to improve their management of agricultural activities such as flowering calendars, harvesting seasons, definition of irrigation methods, nutrition calendars, and programming the times of concentration of production to define the amount of personnel needed for harvesting. The combination of time series and machine learning algorithms based on regression trees (XGBOOST, TR and RF) provides very positive results for the test dataset collected in real conditions for more than a year. The best results were obtained by the XGBOOST model (MAE = 0.03; RMSE = 0.01), and a difference of approximately 0.57% absolute to the main harvest of 2018. [ABSTRACT FROM AUTHOR]

Ireland has a temperate climate influenced by the Atlantic Gulf Stream. This, combined with fertile soils and adequate rainfall, provides an environment ideally suited for grass. Grass is an inexpensive, nutritious feed for livestock, giving Ireland a competitive advantage in this sector. Tillage farming is also an important sector for Ireland, and evidence of the Great Irish Famine of the mid-eighteen hundreds is still visible in the landscape. In this paper, we examine aspects of meteorological involvement in Irish agriculture, from the perspective of the Irish Meteorological Service. We examine the rise of agrometeorology in Ireland and take a look at the development of related services, including potato blight warnings. Two influential characters of Irish agrometeorological history, Austin Bourke and Tom Keane, are examined, as well as organisations such as the AGMET Group. The paper concludes with a brief snapshot of current Irish agrometeorological activities along with future ambitions. [ABSTRACT FROM AUTHOR]

This paper describes the development of the agrometeorology science in Greece from the mid-1970s, when the first "Agrometeorology and Hydrology Division" was established within the Hellenic National Meteorological Service (HNMS), until today. To do so, a series of representative agrometeorology studies conducted mainly by HNMS is discussed in chronological order. Through these studies, one can observe the gradual integration of the fields of agriculture and meteorology and the formulation of a proper multidisciplinary agrometeorology science in Greece, as well as the constant progress in the relative scientific production. Finally, perceiving climate change as a driving force for the conduction of long-term planning for the agricultural sector in Greece, the paper proposes as priorities for the future development of agrometeorology the provision of an adequate network of agro-meteorological stations, the exploitation of satellite information, the reinforcement of the communication between scientists and farmers, and the strengthening of the role of HNMS as a channel for ensuring mutual understanding. [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]

Drought is considered as one of the major natural hazards with a significant impact on agriculture, environment, society and economy. Droughts affect sustainability of agriculture and may result in environmental degradation of a region, which is one of the factors contributing to the vulnerability of agriculture. This paper addresses agrometeorological or agricultural drought within the risk management framework. Risk management consists of risk assessment, as well as a feedback on the adopted risk reduction measures. And risk assessment comprises three distinct steps, namely risk identification, risk estimation and risk evaluation. This paper deals with risk identification of agricultural drought, which involves drought quantification and monitoring, as well as statistical inference. For the quantitative assessment of agricultural drought, as well as the computation of spatiotemporal features, one of the most reliable and widely used indices is applied, namely the vegetation health index (VHI). The computation of VHI is based on satellite data of temperature and the normalized difference vegetation index (NDVI). The spatiotemporal features of drought, which are extracted from VHI, are areal extent, onset and end time, duration and severity. In this paper, a 20-year (1981-2001) time series of the National Oceanic and Atmospheric Administration/advanced very high resolution radiometer (NOAA/AVHRR) satellite data is used, where monthly images of VHI are extracted. Application is implemented in Thessaly, which is the major agricultural drought-prone region of Greece, characterized by vulnerable agriculture. The results show that agricultural drought appears every year during the warm season in the region. The severity of drought is increasing from mild to extreme throughout the warm season, with peaks appearing in the summer. Similarly, the areal extent of drought is also increasing during the warm season, whereas the number of extreme drought pixels is much less than those of mild to moderate drought throughout the warm season. Finally, the areas with diachronic drought persistence can be located. Drought early warning is developed using empirical functional relationships of severity and areal extent. In particular, two second-order polynomials are fitted, one for low and the other for high severity drought classes, respectively. The two fitted curves offer a forecasting tool on a monthly basis from May to October. The results of this drought risk identification effort are considered quite satisfactory offering a prognostic potential. The adopted remote-sensing data and methods have proven very effective in delineating spatial variability and features in drought quantification and monitoring. [ABSTRACT FROM AUTHOR]

Agriculture is the one of the most important priorities and economic activities in developing countries. Climate change, which is already affecting the society at a global level, has an impact on the increase in magnitude and frequency of severe weather events. As such, both agriculture and industry are affected by climate change and their revenues decrease, thus affecting the entire economic cycle. The aim of this paper is to present theoretical considerations regarding climate change induced severe weather events and to highlight some practical techniques used in operational meteorology in order to improve the forecast of severe weather events. By analysing the morphology and dynamics of thunderstorms, by applying the correct parametrization in weather forecast models, nowcasting and by issuing on time weather alerts, financial losses may be limited. Throughout this paper, the parametrization of convective cells will be described, operational forecasting techniques will be presented and the application of ingredient-based forecasting methodology will be discussed. Severe weather associated hazards like hail, heavy rainfall, damaging winds and even frost will be analysed in order to establish a local climatology of these phenomena. With the aid of Weather Surveillance Doppler Radar (WSDR) and by associating some instability convection indices with synoptic charts, the agriculture-related severe weather events can be identified and their formation pattern can be established. Further on, by establishing an analysis methodology and by compelling all available data, weather-related hazard maps are to be compelled and are to be used by agrometeorology forecasters in order to seek viable solutions. The Western part of Romania is characterized as having a moderatecontinental climate, with hot influences from the Southern part of Europe and with humid advections from the South-Western part of Europe. In combination with cold advections from the Northern Europe, both summer as well as winter specific agriculture-related severe weather events are present [ABSTRACT FROM AUTHOR]

This paper highlights the changes brought about by artificial intelligence (AI) systems on business. Developments in this area can complement the human decisionmaking process. Artificial intelligence can reshape companies, management and the innovation process. AI will continue to develop and modify the activity in various economic sectors, due to the high degree of adoption of new automation possibilities. Businesses will have more and more automated processes, but new processes will appear, in which human experts will be also needed. For example, AI may be used in the agricultural sector and may help farmers in their activities, at sowing, in crop and weather monitoring, pest control, harvesting, crop rotation, etc. Thus, AI may help farmers to adopt measures to improve crop quality. [ABSTRACT FROM AUTHOR]

Heat pulse method is a transient method that estimates soil thermal properties by characterizing the radial transport of short-duration line-source heat applied to soils. It has been widely used to measure a wide range of soil physical properties including soil thermal conductivity, thermal diffusivity, heat capacity, water content, ice content, bulk density, water flux and evaporation in laboratory and field environments. Previous studies generally focus on the scientific aspects of heat pulse method based on selected publications, and there is a lack of study investigating the heat pulse publication as a whole. The objective of this study was to give an overall view of the use of heat pulse method for soil physical measurements from the bibliometric perspectives. The analyses were based on the Web of Science Core Collection data between 1992 and 2019 using HistCite Pro and VOSviewer. The results showed an increasing trend in the volume of publications on this field and Dr. Robert Horton was the most productive researcher coauthoring papers on the heat pulse method. The co-authorship analysis revealed that researchers from soil science are closely collaborated, but this is not true for researchers in other fields. There is a lack of new young scientists committing to this field while the older generation of researchers are retiring. The United States Department of Agriculture Agricultural Research Servics (USDA-ARS), the China Agriculture University and the Chinese Academy of Science were the top three organizations applying the heat pulse method, while the USA, China and Canada were the top three countries. The Soil Science Society of America Journal, Water Resources Research and Agricultural and Forestry Meteorology were the most widely used journals. The con-occurrence and citation analysis could be used to map the development of the field and identify the most influential publications. The study showed that the bibliometric analysis is a useful tool to visualize research status as well as to provide the general information of novices and experts alike on the heat pulse method for soil physical measurements. [ABSTRACT FROM AUTHOR]

This paper examines the impact of climate change on the decision of farmers to engage or not to engage in livestock activities and also on the choice of different livestock species in Kenya. To this end, cross-sectional household level data supplemented by long-term averages of climate data are used. The probit model is employed to derive the response of the probability of engaging in livestock activities to climate change. Probit and multivariate probit methods are employed to model the choice of different livestock species. Atmosphere–ocean global circulation models are used to project the impact of different climate scenarios on the probability of engaging in livestock activities and also of adopting different livestock species according to variations in climate. The results suggest that farmers adapt livestock management decisions to climate change. At low levels of temperature increase, the probability of engaging in livestock activities falls, but at higher levels of climate change, the probability rises. The results further show that as it gets hotter, farmers change their livestock choices from dairy cattle and sheep to beef cattle and goats. [ABSTRACT FROM AUTHOR]

Abstract In agrometeorology and management of meteorology related natural resources, many traditional methods and indigenous technologies are still in use or being revived for managing low external inputs sustainable agriculture (LEISA) under conditions of climate variability. This paper starts with the introduction of an end-to-end climate information build up and transfer system in agrometeorology, in which the use of such methods and technologies must be seen to operate. It then reviews the options that LEISA farmers have in risk management of agrometeorological and agroclimatological calamities. This is based on the role that the pertinent meteorological/climatological parameters and phenomena play as limiting factors in agricultural production and the expectations on their variability. Subsequently, local case studies are given as examples of preparedness strategies to cope with i). variable water/moisture flows, including mechanical impacts of rain and/or hail, ii). variable temperature and heat flows, including fires, and iii). fitting cropping periods to the varying seasons, everywhere including related phenomena as appropriate. The paper ends with a series of important additional considerations without which the indicated strategies cannot be successful on a larger scale and in the long run. [ABSTRACT FROM AUTHOR]

• Over 60 y, the scope of phenological research published in AFM has evolved. • Agricultural applications were once dominant; climate change is now a recurring theme. • Review focuses on Phenocam, or phenology camera, papers published in AFM. • Documents history and evolution of the PhenoCam Network, and lessons learned. • Opportunities for machine learning/computer vision to advance Phenocam science. Over the last twenty years, phenology—the study of seasonal life cycle events—has emerged as a key subfield of global change biology. Phenology provides an integrated measure of the organismal response to climate change and is a key driver of the functional responses of ecosystems to climate change. Since I established the PhenoCam Network in 2008, over 200 papers have been published using Phenocam technology, and these papers have added to our understanding of phenology as both an indicator of climate variability and change and a key aspect of ecosystem function. This review examines: (1) the changing phenological research landscape, as represented by phenology-themed papers in Agricultural and Forest Meteorology (AFM), over the last 60 y; (2) the contributions of phenocams and the PhenoCam Network, as reported in the pages of AFM, to the study of phenology; and (3) the lessons I have learned from developing this grassroots effort, and how other researchers might benefit from the PhenoCam Network's successes and failures. Key conclusions to emerge from this review include: (1) the enormous, value-added power of research networks; (2) the importance of both interpersonal relationships and serendipity, in the metamorphosis of ideas into results; and (3) the potential for open, freely-available data to be transformative, in ways that cut across disciplinary, socioeconomic, and demographic barriers. Finally, the development of the PhenoCam Network has been a collaborative, multidisciplinary experiment in team science, and the commitment of my team members and the enthusiasm of my collaborators have been critical to the success of these efforts. [ABSTRACT FROM AUTHOR]

The current study will present the effect of the main weather conditions on the growth and fruit bearing in apricot tree. Within this framework, an analysis is made into the geographical distribution of the main characteristics of the temperatures, precipitation as well as insolation during the spring season with the respect to the flower-cladding and the effect of low April temperatures upon fruit bearing. Considering the fact that the study of the peculiarities of each variety of apricot is a complex process and due to the bioclimatic conditions, that must be met in order to have both a qualitative as well as a quantitative culture, the meteorological characteristics play an important role in the growth and fruiting of the apricot. The purpose of this article is to analyse the agrometeorological characteristics of the fruit bearing seasons for the western part of Romania. By analysing both the synoptic and the agrometeorological indices, a framework can be established in order to present a pertinent study in the field of agrometeorology with high regard on apricot growth and fruit bearing. The analysis of severe weather events associated with early to late spring solar radiation is also presented in this paper because of the negative effects of hail and it is included in the final framework of this study. [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]

Climate change and extreme weather events are threatening agricultural production worldwide. The anticipated increase in atmospheric temperature may reduce the potential yield of cultivated crops. Agroforestry is regarded as a climate-resilient system that is profitable, sustainable, and adaptable, and has strong potential to sequester atmospheric carbon. Agroforestry practices enhance agroecosystems' resilience against adverse weather conditions via moderating extreme temperature fluctuations, provisioning buffers during heavy rainfall events, mitigating drought periods, and safeguarding land resources from cyclones and tsunamis-type events. Therefore, it was essential to comprehensively analyze and discuss the role of agroforestry in providing resilience during extreme weather situations. We hypothesized that integrating trees in to the agro-ecosystems could increase the resilience of crops against extreme weather events. The available literature showed that the over-story tree shade moderates the severe temperature (2-4°C) effects on understory crops, particularly in the wheat and coffee-based agroforestry as well as in the forage and livestock-based silvipasture systems. Studies have shown that intense rainstorms can harm agricultural production (40-70%) and cause waterlogging. The farmlands with agroforestry have been reported to be more resilient to heavy rainfall because of the decrease in runoff (20-50%) and increase in soil water infiltration. Studies have also suggested that drought-induced low rainfall damages many crops, but integrating trees can improve microclimate and maintain crop yield by providing shade, windshield, and prolonged soil moisture retention. The meta-analysis revealed that tree shelterbelts could mitigate the effects of high water and wind speeds associated with cyclones and tsunamis by creating a vegetation bio-shield along the coastlines. In general, existing literature indicates that implementing and designing agroforestry practices increases resilience of agronomic crops to extreme weather conditions increasing crop yield by 5-15%. Moreover, despite its widely recognized advantages in terms of resilience to extreme weather, the systematic documentation of agroforestry advantages is currently insufficient on a global scale. Consequently, we provide a synthesis of the existing data and its analysis to draw reasonable conclusions that can aid in the development of suitable strategies to achieve the worldwide goal of adapting to and mitigating the adverse impacts of climate change. [ABSTRACT FROM AUTHOR]

As part of a concerted research effort to design management strategies for an economic insect pest of cashew (Anacardium occidentale L.), viz., Analeptes trifasciata Fabricius (Coleoptera: Cerambycidae), also known as the stem girdler, it became necessary to understand the biotic and abiotic factors influencing its population fluctuation. The population dynamics of A. trifasciata and its relationship with crop phenology (flushing, senescence, flower initiation, fruit development and maturation stages) and weather factors (average temperature, relative humidity and rainfall) were investigated from 2017 to 2019 at the cashew orchard of Cocoa Research Institute of Nigeria (CRIN), Ibadan, Nigeria. Observations were recorded at monthly intervals for the duration. The period of incidence was from May to December. Peak abundance, which coincided with the wet season as well as senescence and flower initiation stages, were not significantly correlated. Adult abundance of A. trifasciata was conspicuously low during the fruit development and maturation stages, this stage coincided with the peak of the dry season. However, there was no significant correlation between the pest abundance and crop phenological stages. The male to female ratio was 1:0.81 (÷2 =1.313, df =1, p =0.252). The abundance of A. trifasciata significantly but negatively correlated (r = -0.73) with temperature. Relative humidity (r = 0.30) and rainfall (r = 0.24) showed non-significant association with stem-girdler population. Our findings indicated that temperature is the major predictor of A. trifasciata population fluctuation irrespective of the phenological stage. These findings can be useful for developing a forecasting model for its early detection and appropriate management. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) and above ground biomass (AGB) are two parameters that are difficult to measure but very useful. In this paper we investigated the relationship between coffee biophysical properties and LAI and AGB in two coffee production systems: full sun (FS) and shaded with macadamia nuts (SH). The paper proposes an empirical relationship for calculating coffee AGB and coffee LAI which avoids destructive methods, using simple field measurements and agrometeorological data. Here, we reported that LAI is related to canopy structure but subject to strong seasonal variations, which can be identified using water requirements satisfaction index (WRSI). Coffee LAI answers to the decreased WRSI with 1 month lag (WRSI-1) and LAI values decreases more for FS systems than for SH systems during dry periods. The best empirical model to predict LAI for FS coffee production system was based on canopy height (ch) and WRSI-1 value. For SH systems, the best model used ch, WRSI-1 and the height of the first pair of branches. Coffee AGB values were measured using destructive analyses and an empirical equation was developed. Both coffee production systems stocked carbon, whereas the SH system stocked an increased carbon amount provided by the macadamia trees that contributed with 15 % of the total carbon above ground. Both systems can be considered mitigation techniques since they are able to remove atmospheric carbon and stock it in the biomass, which has been largely proposed as a compensation mechanism for greenhouse gas emissions. [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]

This paper evaluated the spatiotemporal non-stationarity in the vegetation dynamic based on 1-km resolution 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets in China during 2001-2011 through a wavelet transform method. First, it revealed from selected pixels that agricultural crops, natural forests, and meadows were characterized by their distinct intra-annual temporal variation patterns in different climate regions. The amplitude of intra-annual variability generally increased with latitude. Second, parameters calculated using a per-pixel strategy indicated that the natural forests had the strongest variation pattern from seasonal to semiannual scales, and the multiple-cropping croplands typically showed almost equal variances distributed at monthly, seasonal, and semiannual scales. Third, spatiotemporal non-stationarity induced from cloud cover was also evaluated. It revealed that the EVI temporal profiles were significantly distorted with regular summer cloud cover in tropical and subtropical regions. Nevertheless, no significant differences were observed from those statistical parameters related to the interannual and interannual components between the de-clouded and the original MODIS EVI datasets across the whole country. Finally, 12 vegetation zones were proposed based on spatiotemporal variability, as indicated by the magnitude of interannual and intra-annual dynamic components, normalized wavelet variances of detailed components from monthly to semiannual scale, and proportion of cloud cover in summer. This paper provides insightful solutions for addressing spatiotemporal non-stationarity by evaluating the magnitude and frequency of vegetation variability using monthly, seasonal, semiannual to interannual scales across the whole study area. [ABSTRACT FROM AUTHOR]

John Sheard and Graham Harris were both PhD students under Ken's supervision working on the genus I Rinodina i [11] and the ecophysiology of epiphytic lichens[12], respectively. Ken was not a co-author of their published output, just as Peter Greig-Smith had not been a co-author of Ken's, but this practice changed thereafter. In the late 1960s, Ken began working on nitrogen fixation in lichens in collaboration with John Millbank, a microbial biochemist at Imperial. The main thrust of this research was the identification of pattern in lichen-dominated plant communities and explaining the observed patterns in terms of variation in, and lichen response to, microclimate. [Extracted from the article]

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]

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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]

A long-term experiment was conducted during kharif seasons of2013 to 2020 at AICRP on Agrometeorology, VNMKV, Parbhani, to study the crop-weather relationship of soybean grown under four environments (27th SMW, 28th SMW, 29th SMW and 30th SMW) with three soybean varieties (MAUS-158, MAUS-71, and JS-335). Results indicated that soybean yield was significantly highest in (1541.4 kg ha-1) in the early sown crop, and it decreased with delay in sowing with the lowest yield (803.8 kg ha-1). Among the cultivars, MAUS-158 produced significantly the highest yield (1321.3 kg ha-1). Correlation analysis between weather parameters during different phases with the yield revealed that the vegetative and grain formation period of soybean were found to be the most sensitive to weather parameters, as the correlation coefficients with most of the weather parameters. Rainfall, number of rainy days, humidity and soil moisture had favorable effects while temperatures and sunshine duration had negative effects on the yield of soybean in the Marathwada region. Individual weather parameters during 50% flowering to maturity period could explain 40 to 55% variation in the soybean yield while the multiple regression developed all weather parameters during grain formation to physiological maturity period could explain up to 82% variation which could be used the predict the soybean yield in Marathwada region. [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]

The authors of the present paper studied some aspects of hydro and agrometeorological phenomena in terms of humidness, dryness and crop growing periods over India. Revised water balance model is used to obtain the above and the results are examined during the two half centuries i.e 1901 to 1950 and 1951 to 1995 in delineating the climate change impacts over India. Palmer Drought Severity Index (PDSI) for All India is related to aridity and humidity indices to understand the dry and wet spells over India. The paper also holds its stand on the analysis and the trends of thermo and hygric components such as precipitation, maximum and minimum temperatures, water need, actual evapotranspiration and soil moisture for All India. The return rates of All India maximum and minimum rainfall and temperatures are obtained for different time intervals using Extreme Value Analysis, that might be useful in assessing the impact and thereby to plan over the risk management for the better environmental and thus the human sustainability. [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]

This paper reports the development of a neurocomputingbased model for estimating the potential evapotranspiration over Gangetic West Bengal, India during the summer monsoon months of June, July and August. An artificial neural network is implemented in the form of multilayer perceptron to generate the model. Three weather variables, surface temperature, vapour pressure and rainfall are used as the independent variables in generating the model. The performance of the model is judged statistically against nonlinear regression in the form of asymptotic regression. The study reveals that an artificial neural network is more efficient than the regression approach to estimate the potential evapotranspiration in the summer monsoon months. Furthermore, it is established that the artificial neural network and nonlinear regression have almost equal efficiency in the previously mentioned estimation in the month of June. However, in July and August the higher values of correlation and Willmotts indices, and lower values of estimation error, indicate that the artificial neural network is more reliable than the nonlinear regression approach. Since evapotranspiration is one of the basic components of the hydrological cycle and is essential for estimating irrigation water requirement, an efficient estimation procedure may help in agrometeorological modelling and irrigation scheduling in the summer monsoon months, which are of high importance for agriculture in the study zone. Copyright © 2009 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

This paper examines the distribution of climate change impacts across the sixteen Agro-Ecological Zones (AEZs) of Africa. We combine net revenue from livestock and crops and regress total net revenue on a set of climate, soil, and socio-economic variables with and without country fixed effects. Although African crop net revenue is very sensitive to climate change, combined livestock and crop net revenue is more climate resilient. With the hot and dry CCC climate scenario, average damage estimates reach 27% by 2100, but with the mild and wet PCM scenario, African farmers will benefit. The analysis of AEZs implies that the effects of climate change will be quite different across Africa. For example, currently productive areas such as dry/moist savannah are more vulnerable to climate change while currently less productive agricultural zones such as humid forest or sub-humid AEZs become more productive in the future. [ABSTRACT FROM AUTHOR]

This paper analyzes the long-term (1901–2002) temporal trends in the agroclimate of Alberta, Canada, and explores the spatial variations of the agroclimatic resources and the potential crop-growing area in Alberta. Nine agroclimatic parameters are investigated: May–August precipitation (PCPN), the start of growing season (SGS), the end of the growing season (EGS), the length of the growing season (LGS), the date of the last spring frost (LSF), the date of the first fall frost (FFF), the length of the frost-free period (FFP), growing degree-days (GDDs), and corn heat units (CHUs). The temporal trends in the agroclimatic parameters are analyzed by using linear regression. The significance tests of the trends are made by using Kendall’s tau method. The results support the following conclusions. 1) The Alberta PCPN has increased 14% from 1901 to 2002, and the increment is the largest in the north and the northwest of Alberta, then diminishes (or even becomes negative over two small areas) in central and southern Alberta, and finally becomes large again in the southeast corner of the province. 2) No significant long-term trends are found for the SGS, EGS, and LGS. 3) An earlier LSF, a later FFF, and a longer FFP are obvious all over the province. 4) The area with sufficient CHU for corn production, calculated according to the 1973–2002 normal, has extended to the north by about 200–300 km, when compared with the 1913–32 normal, and by about 50–100 km, when compared with the 1943–72 normal; this expansion implies that the potential exists to grow crops and raise livestock in more regions of Alberta than was possible in the past. The annual total precipitation follows a similar increasing trend to that of the May–August precipitation, and the percentile analysis of precipitation attributes the increase to low-intensity events. The changes of the agroclimatic parameters imply that Alberta agriculture has benefited from the last century’s climate change. [ABSTRACT FROM AUTHOR]

This paper uses comparable income aggregates from 41 national household surveys from 22 countries to explore the patterns of income generation among rural households in Sub-Saharan Africa, and to compare household income strategies in Sub-Saharan Africa with those in other regions. The paper seeks to understand how geography drives these strategies, focusing on the role of agricultural potential and distance to urban areas. Specialization in on-farm activities continues to be the norm in rural Africa, practiced by 52 percent of households (as opposed to 21 percent of households in other regions). Regardless of distance and integration in the urban context, when agro-climatic conditions are favorable, farming remains the occupation of choice for most households in the African countries for which the study has geographically explicit information. However, the paper finds no evidence that African households are on a different trajectory than households in other regions in terms of transitioning to non-agricultural based income strategies. [ABSTRACT FROM AUTHOR]

Aspects of Agriculture and Animal husbandry have been dealt in literature beginning from the Harappan civilization. The Vedic and Post- Vedic texts reflect vast information about rain ceremonies, rites for cattle, agricultural rites, and crop prosperity. This knowledge was supplemented practically by mentioning agricultural technology, crops, sowing and harvesting practices that still are very important to Modern agriculture research. One must also appreciate the agricultural philosophy of our ancestors that reflect their harmonious living with nature sustaining their livelihoods with such agricultural practices that brought prosperity to men, animals, plants, and also the environment. These had strong roots in worship of the deities of Nature seeking their grace in various agricultural rites. Associated with these practices was a sound knowledge of agricultural meteorology that allowed sustained agriculture synchronized with Nature's seasonal cycles and phenomena. These observations have been recorded by Sages and other learned scholars in their vast literature. The SangrahaShiromani is one such astrological text that deals on agricultural meteorology in certain sections. The purpose of the paper is to highlight this knowledge so that interdisciplinary studies based on the information will bring about refinement and revolution in our present agricultural practices that are based on the indigenous traditions harmonious to all living beings of the Cosmos. A review of various sections of the text was carried out and the features mentioned therein are presented here. [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]