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The paper presents the behaviour of some wheat varieties under the years 2014, 2016, 2018, 2019, 2020, 2023 conditions in Dobrogea area in demonstrative plots carried out at S.C. SPORT AGRA S.R.L. Amzacea, Constanta County. The main aspects emphasized in this research area have been: climatic conditions, obtained yield, and economical efficiency. Looking to the pathogens, for wheat Septoria tritici showed an AR between 17-27% and Pyrenophora graminea showed a low atack (AR = 4.5-17.5%). The pathogen Puccinia striiformis was present in low precentage (AR = 0-13.5%) in the 2018 April observations. For crop protection against pathogens, 2 foliar tratments with fungicides were applied, Artea 330 EC(ciproconazol 80 g/l + propiconazol 250 g/l) 0,4 l/ha, in March and Priaxor EC (fluxapiroxad 150 g/l + piraclostrobin 75 g/l) 1l/ha and in April respectively. Fungal treatments have stopped the development of foliar and ear diseases. The beneficial effects were found in the good yields obtained in 2014, 2016, 2018, 2019, except the years 2020 and 2023. In 2020 Otilia reached 2,370 kg/ha, Miranda 2,260 kg/ha and Solindo variety 4,297 kg/ha and Ursita 4,165 kg/ha in 2023. All these special yields have been obtained under non-irrigated conditions. [ABSTRACT FROM AUTHOR]

The article presents information on a study conducted on the impact of climate change on agriculture. In the study, Community Land Model (CLM)-Crop was validated against observations from two agricultural sites in the U.S., that were planted with maize and soybean. CLM-Crop was compared with the standard CLM3.5, which is associated with crops as grass. The study found that crop yields and productivity had a negative correlation with temperature, and a positive correlation with precipitation.

Affected by global warming, the frequency of crop pests and diseases have increased, causing huge losses to agricultural production. To better grasp the development and trends of research on the effects of climate change on crop pests and diseases, the literature on the impact of climate change on crop pests and diseases published from 1990 to 2021 in the Web of Science (WOS) core collection database was used. This study explores the literature characteristics and hotspot evolution through the bibliometric visualization analysis software COOC, VOSviewer, and CiteSpace, with a view to identifying the changing characteristics and trends of research changes in this field. The results showed that the number of literature on the impact of climate change on crop pests and diseases increased rapidly. The main fields involved include environmental sciences, ecology, and agronomy. Papers in these fields mainly published in journals, such as PLos One, Forest Ecology and Management, and Frontiers in Plant Science. The country with the highest number of publications was the United States, followed by China and Australia. The most prolific authors in the top 20 are research scholars from China. The first author of the top 20 highly cited papers was from the United States. It was found that that current research on the impact of climate change on crop pests and diseases mainly focuses on agricultural production and food safety. Modelling and crop growth has maintained steady development. At present, research in this field mainly focuses on pest management strategies under the impact of climate change, the response of single species, and the complex ecological mechanisms behind the response. This study provides unique insights into the research field of the impact of climate change on crop pests and diseases and provides a reference direction for future research development in this field. [ABSTRACT FROM AUTHOR]

The availability of coherent time series of crop statistics is essential for better analyzing the past, understanding the present, and predicting future trends in yield, area, and production. Importantly, such data also underpin assessments and subsequent policy actions that can shape future food systems that are more resilient in the face of climate change and that are sustainable in terms of resource use efficiency. In the European Union (EU), there currently is no legal obligation for EU countries to provide subnational crop statistics. However, such data could improve in-season crop forecasts, climate change impacts and adaption need assessments, and evaluation of agri-environmental schemes. The dataset described in this paper includes a harmonized collection of subnational crop statistics on area, production, and yield, collected for the EU from National Statistical Institutes (NSIs) and the EUROSTAT REGIO database – subnational crop statistics voluntarily contributed by EU countries. The crops considered are wheat (including soft and durum wheat), barley (including winter and spring barley), grain maize, sunflower, and sugar beet. All data are harmonized towards the hierarchical structure of the EUROSTAT legend and the regional classification of NUTS (Nomenclature of Territorial Units for Statistics) version 2016, to provide coherent time series. A total of 344 282 records is presented here (115 974 for wheat, 122 705 for barley, 35 274 for grain maize, 34 916 for sugar beet, and 35 413 for sunflower) for a total of 961 regions in 27 EU countries. Statistics are reported from 1975 to 2020, with a median time-span range of 21 years. A flagging system details, for each data record, information on data sources, processing steps, and quality-checking results. This includes consistency checks between reported values for area, yield and production, identification of null values, missing and calculated data, information on crop legend matching, and NUTS versioning. We illustrate the value of this dataset by analyzing impacts on crops and production zone shifts in Europe due to climatic and economic factors. Recommendations and future developments of collecting subnational statistics at the EU level are briefly discussed. The dataset is accessible with European Commission Authentication Service (ECAS) login at 10.2905/685949ff-56de-4646-a8df-844b5bb5f835. [ABSTRACT FROM AUTHOR]

Recently presented climate change scenarios include increasing demand for water, due to both increase in temperature and variations in precipitation pattern. Since crop water requirements are related to climate and irrigation scheduling depends on crop evapotranspiration (ETc), it is crucial to have tools that allow the estimation of ETc in a reliable way. ETc is often calculated as the product of two factors, the reference evapotranspiration (ETo) and crop coefficient (Kc) values that convert ETo to ETc. This paper reviews Kc values, measured in different parts of the world and in different climates, with the aim to offer a practical tool for selecting the most appropriate Kc values for water balance irrigation scheduling, which result in more efficient irrigation. More than 100 scientific papers were studied and summarized in a report, accompanied by an extensive Kc database. The Kc report and database are available in the "Supplemental Data" section of this paper. [ABSTRACT FROM AUTHOR]

The fast human climate change we are witnessing in the early twenty-first century is inextricably linked to the health and function of the biosphere. Climate change is affecting ecosystems through changes in mean conditions and variability, as well as other related changes such as increased ocean acidification and atmospheric CO2 concentrations. It also interacts with other ecological stresses like as degradation, defaunation, and fragmentation.Ecology and climate monitoring are critical to understanding the complicated interactions between ecosystems and changing climate trends. This review paper dives into the issues of ecological and climate monitoring, emphasizing the complications caused by technical limits, data integration, scale differences, and the critical requirement for accurate and timely information. Understanding the ecological dynamics of these climatic impacts, identifying hotspots of susceptibility and resistance, and identifying management measures that may aid biosphere resilience to climate change are all necessary. At the same time, ecosystems can help with climate change mitigation and adaptation. The processes, possibilities, and constraints of such nature-based climate change solutions must be investigated and assessed. Addressing these issues is critical for developing successful policies and strategies for mitigating the effects of climate change and promoting sustainable ecosystem management. Human actions inscribe their stamp in the big narrative of our planet's story, affecting the very substance of the global atmosphere. This transformation goes beyond chemistry, casting a spell on the physical characteristics that choreograph Earth's brilliant dance. These qualities, like heavenly notes, create a song that echoes deep into the biosphere. We go on a journey via recorded tales of ecological transformation as they respond to the ever-shifting environment in this text. We peek into the rich fabric of change, drawing insight from interconnected observatories. Nonetheless, this growing symphony is set to unleash additional transformational stories - narratives of natural riches and rhythms that are both economically and environmentally essential. Understanding these stories is essential for navigating this developing epic. A roadmap for sustainable development necessitates the ability to comprehend these stories, a problem that resonates across the breadth of monitoring programs, particularly in the infancy of integrated sites. [ABSTRACT FROM AUTHOR]

Drought and heat are two stresses that often occur together and may pose significant risks to crops in future climates. However, the combined effects of these two stressors have received less attention than single-stressor investigations. This study used a rapid and straightforward phenotyping method to quantify the variation in 128 African eggplant genotype responses to drought, heat, and the combined effects of heat and drought at the seedling stage. The study found that the morphophysiological traits varied significantly among the 128 eggplants, highlighting variation in response to abiotic stresses. Broad-sense heritability was high (> 0.60) for chlorophyll content, plant biomass and performance index, electrolyte leakage, and total leaf area. Positive and significant relationships existed between biomass and photosynthetic parameters, but a negative association existed between electrolyte leakage and morpho-physiological traits. The plants underwent more significant stress when drought and heat stress were imposed concurrently than under single stresses, with the impact of drought on the plants being more detrimental than heat. There were antagonistic effects on the morphophysiology of the eggplants when heat and drought stress were applied together. Resilient genotypes such as RV100503, RV100501, JAMBA, LOC3, RV100164, RV100169, LOC 3, RV100483, GH5155, RV100430, GH1087, GH1087*, RV100388, RV100387, RV100391 maintained high relative water content, low electrolyte leakage, high Fv/Fm ratio and performance index, and increased biomass production under abiotic stress conditions. The antagonistic interactions between heat and drought observed here may be retained or enhanced during several stress combinations typical of plants' environments and must be factored into efforts to develop climate change-resilient crops. This paper demonstrates improvised climate chambers for high throughput, reliable, rapid, and cost-effective screening for heat and drought and combined stress tolerance in plants. [ABSTRACT FROM AUTHOR]

Based on the climate proxy data, several recent studies have concluded that the Ming dynasty's reign in China coincided with the Little Ice Age, a global crisis. In response, scholars have published several reports in recent years addressing this topic. This paper presents a comprehensive overview of the current research findings in English regarding this subject and identifies existing research gaps. The author proposes that the impact of climate on different regions during the late Ming period remains largely underexplored. Furthermore, scholars must exercise caution when assuming that adverse climatic conditions uniformly impacted the Ming empire during the Little Ice Age. This paper also highlights the use of simplistic models by scholars linking cold and dry climates to crop failure, floods, droughts, population decline, and other factors. However, any straightforward models that presume causal determination risk ignoring historical facts. [ABSTRACT FROM AUTHOR]

Crop quality is directly related to national food security and people's living standards, and it is also key to the improvement of agricultural quality and efficiency. According to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC), climate change has already exerted a negative impact on crop quality. To address climate change scientifically, this paper reviews the latest progress in studies on the impact of climate change on crop quality, and points out limitations of existing studies: (1) Climate factors affecting crop quality are not yet clearly identified; (2) The climate change influencing mechanism and disaster-inducing critical meteorological conditions for crop quality are not clearly established; and (3) No climatic suitability model for crop quality has been constructed to reflect the synergy of multiple climate factors. To ensure food quality and security, and to adjust and optimize the industrial planning of grain crops, promote a high crop quality and yield, and address climate change scientifically, this paper argues that subsequent studies should discuss the following topics, focusing on the climatic suitability of crop quality and resistance mechanisms: (1) changing laws involving the climate sensitivity of crop quality in the context of climate change; (2) response mechanisms of crop quality to climate change; (3) crop quality monitoring and assessment modelling; (4) climatic suitability zoning of crop quality; (5) spatiotemporal evolution trends of crop quality and its responses; and (6) crop quality and its legal measures in the world. [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]

According to the climate change scenario, climate change in the Korean Peninsula is expected to worsen due to extreme temperatures, with effects such as rising average temperatures, heat waves, and droughts. In Republic of Korea, which relies on foreign countries for the supply of forage crops, a decrease in the productivity of forage crops is expected to cause increased damage to the domestic livestock industry. In this paper, to solve the issue of climate vulnerability for forage crops, we performed a study to predict the productivity of forage crops in relation to climate change. We surveyed and compiled not only forage crop production data from various regions, but also experimental cultivation production data over several years from reports of the Korea Institute of Animal Science and Technology. Then, we crawled related climate data from the Korea Meteorological Administration. Therefore, we were able to construct a basic database for forage crop production data and related climate data. Using the database, a production prediction model was implemented, applying a multivariate regression analysis and deep learning regression. The key factors were determined as a result of analyzing the changes in forage crop production due to climate change. Using the prediction model, it could be possible to forecast the shifting locations of suitable cultivation areas. As a result of our study, we were able to construct electromagnetic climate maps for forage crops in Republic of Korea. It can be used to present region-specific agricultural insights and guidelines for cultivation technology for forage crops against climate change. [ABSTRACT FROM AUTHOR]

In recent years, the adverse effect of climate change on soil properties in the agricultural sector has become a dreadful reality worldwide. Climate change-induced abiotic stresses such as salinity, drought and temperature fluctuations are devastating crops' physiological responses, productivity and overall yield, which is ultimately posing a serious threat to global food security and agroecosystems. The applications of chemical fertilizers and pesticides contribute towards further deterioration and rapid changes in climate. Therefore, more careful, eco-friendly and sustainable strategies are required to mitigate the impact of climate-induced damage on the agricultural sector. This paper reviews the recently reported damaging impacts of abiotic stresses on various crops, along with two emerging mitigation strategies, biochar and biostimulants, in light of recent studies focusing on combating the worsening impact of the deteriorated environment and climate change on crops' physiological responses, yields, soil properties and environment. Here, we highlighted the impact of climate change on agriculture and soil properties along with recently emerging mitigation strategies applying biochar and biostimulants, with an aim to protecting the soil, agriculture and environment. [ABSTRACT FROM AUTHOR]

Climate change and human activities have massively impacted the hydrological cycle. Thus, it is of the greatest concern to examine the effect of climate change on water management, especially at the regional level, to understand the possible future shifts in water supply and water-related crises and support regional water management. Fortunately, there is a high degree of ambiguity in determining the effect of climate change on water requirements. In this paper, the statistical downscaling (SDSM) model is applied to simulate the potential impact of climate on crop water requirements (CWR) by downscaling ET0 in the region of Western Maharashtra, India, for the future periods, viz., the 2030s, 2050s, and 2080s, across three meteorological stations (Pune, Rahuri, and Solapur). Four crops, i.e., cotton, soybean, onion, and sugarcane, were selected during the analysis. The Penman-Monteith equation calculates reference crop evapotranspiration (ET0). Furthermore, in conjunction with the crop coefficient (Kc) equation, it calculates crop evapotranspiration (ETc)/CWR. The predictor variables were extracted from the National Centre for Environmental Prediction (NCEP) reanalysis dataset for 1961–2000 and the HadCM3 for 1961–2099 under the H3A2 and H3B2 scenarios. The results indicated by SDSM profound good applicability in downscaling due to satisfactory performance during calibration and validation for all three stations. The projected ET0 indicated an increase in mean annual ET0 compared to the present condition during the 2030s, 2050s, and 2080s. The ET0 would increase for all months (in summer, winter, and pre-monsoon seasons) and decrease from June to September (monsoon season). The estimated future CWR shows variation in the range for cotton (− 0.97 to 2.48%), soybean (− 2.09 to 1.63%), onion (0.49 to 4.62%), and sugarcane (0.05 to 2.86%). The significance of this research lies in its contribution to understanding the potential impacts of climate change at a regional level. This study provides valuable insights into the expected changes in water demand for key crops. The research also manifests implementing an identical methodology for downscaling other environmental parameters using a similar approach. [ABSTRACT FROM AUTHOR]

The paper analyzed the status of Sorghum cultivated area, production and yield at the EU-28 level and in each producing country in the period 2008-2019, and then the forecast for these indicators was estimated for the horizon 2020-2024. Fixed index, trend line, descriptive statistics and regression modeling were used to process the data. The cultivated surface with sorghum in the EU reached 197 thousand ha in 2019, being almost double than on 2008. The cultivated area with Sorghum increased in all sorghum producing countries: France, Italy, Hungary, Romania, Bulgaria, Austria and Greece, except Spain and Slovakia were it remained constant. The largest cropped areas are in France, Italy and Romania, accounting for 76%. Sorghum output doubled its figure and in 2019 reached 1,019 thousand tons. About 91% of production being achieved by France (431), Italy (312), Hungary (117) and Romania (68). The EU yield was 5,179 kg/ha, but higher yields were recorded by Austria, Italy and France. In 2024, it is estimated that the EU will produce 1,045 thousand tons sorghum. Production will increase in Italy, Austria, Bulgaria, Romania and Greece, will decline in France and Hungary and will remain constant in Spain and Slovakia. The EU will continue to pay attention to this crop as a response to climate change. The main purposes of sorghum being animal feed, biomass for bio-fuels, raw material in food industry, component in crop rotation, maintenance of biodiversity and environment preservation. [ABSTRACT FROM AUTHOR]

This paper proposes a coordinated tracking controller for a tractor-trailer to keep a desired distance and heading angle relative to a combine harvester for the autonomous unloading of harvested crops in automatic agriculture operations. A coordinate transformation and the prescribed performance control (PPC) technique are employed to develop a second-order Euler-Lagrange formulation of the tracking errors. Then, a neural adaptive saturated velocity observer-based controller is proposed to force that the trailer center is exactly placed under the end point of the harvester unloading tube to collect cereal grains. By an effective application of the PPC technique and computing a safe motion boundary, controller non-singularity, collision avoidance and connectivity between two vehicles are preserved continuously. The model uncertainties including unknown vehicle parameters, variable trailer mass and moment of inertia during the crop collection, surface friction, climate and crop conditions and external disturbances are compensated by an effective combination of a neural network and an adaptive robust control law. Lyapunov’s direct method is employed to prove that the tracking errors are semi-globally uniformly ultimately bounded and converge to a neighborhood of the origin with a pre-specified maximum overshoot, convergence rate and steady-state accuracy. Finally, comparative simulation results are presented to demonstrate the effectiveness of the proposed controller for the crop harvesting operations in a practical perspective. [ABSTRACT FROM AUTHOR]

According to the current Intergovernmental Panel of Climate Change report, climate change will increase the probability of occurrence of droughts in some areas. Recent contributions at the international level indicate that trade is expected to act as an efficient tool to mitigate the adverse effect of future climate conditions on agriculture. However, no contribution has focused on the similar capacity of trade within any country yet. The U.S. is an obvious choice given that many climate impact studies focus on its agriculture and around 90% of the U.S. crop trade is domestic. Combining a recent state‐to‐state trade flow dataset with detailed drought records at a fine spatial and temporal resolution, this paper highlights first that trade increases as the destination state experiences more drought and inversely in the origin state. As a result, crop growers' profits depend on both local and trade partners' weather conditions. Projections based on future weather data convert the crop grower's expected loss without trade into expected profit. As such, this paper challenges the estimates of the current climate impact literature and concludes that trade is expected to act as a $14.5 billion mitigation tool in the near future. [ABSTRACT FROM AUTHOR]

The paper presents the estimated changes in the soil water content, the total runoff, the sediment yield and the actual evapotranspiration for the small Bystra catchment in the east of Poland. The findings are based on the results of three simulations covering the years of 2041–2050. The simulations were based on a calibrated and validated SWAT model (2010–2017). The first variant covers just the climate change and the existing structure of soil cultivation for the three regional climate models supported by the EC-EARTH global climate model in the emission scenarios RCP4.5 and RCP8.5. Variants two and three are based on the first variant in terms of the changing climate. The second variant, however, involves placing a pond in each farm in the catchment, while the third variant involves designing huge reservoirs as a result of land consolidation. Variants two and three occur in five adaptation scenarios each. The first adaptation scenario (V2.1 and V3.1) involves only increasing the number of ponds on the farm or increasing the number of reservoirs for non-irrigated arable land crops, i.e., WWHT (winter cereals), BARL (spring cereals), CANP (rapeseed) and CRDY (other crops). The second adaptation scenario (V2.2 and V3.2) involves growing vegetables without irrigation (instead of cereals). The third adaptation scenario (V2.3 and V3.3) involves growing vegetables with irrigation (instead of cereals). The fourth adaptation scenario (V2.4 and V3.4) involves partial cultivation of vegetables and cereals. The fifth adaptation scenario (V2.5 and V3.5) involves partial cultivation of orchards and cereals. The adaptation scenarios of the irrigation of vegetables from deep water-bearing layers (second variant) or reservoirs (third variant) contribute to the increase in water content in the soil, especially in summer, in comparison with the adaptation scenarios for vegetable cultivation without irrigation. What is more, the actual evapotranspiration was higher in the adaptation scenarios involving irrigation than in scenarios without irrigation. It is known that the changes in water content in soil and the intensification of water erosion are gravely affected by modifications in crops and soil cultivation. A change from cereal cultivation to irrigated vegetable cultivation or orchards increased the water content in the soil in most climatic projections. However, the increase in the number of ponds in the second variant had little impact on the soil water content, actual evapotranspiration and overall runoff, while the erosion loss decreased. With the lower precipitation levels in the years 2041–2050 relative to 2010–2017, as presented in the emissive scenario RCP 4.5, the soil water content decreases by up to 14% for most variants. Total runoff for most variants will also be lower by 4–35%. The percentage change in sediment yield will fluctuate between −86% and 116%. On the other hand, the actual evapotranspiration for most variants will be higher. With higher precipitation levels in the years 2041–2050 relative to 2010–2017, as presented in the emissive scenario RCP 8.5, the soil water content changes slightly from −7% to +3%. Total runoff for most variants will also be higher by as much as 43%. Sediment yield for most scenarios may increase by 226%. The actual evapotranspiration for most variants will also be higher. Irrigation variants tend to increase soil available water while increasing evapotranspiration and total outflow in the catchment as compared to non-irrigated LULC. The largest increase in the soil water content is observed in most irrigation variants for RCP 4.5 (annual average 316–319 mm) (V2.3-V2.5, V3.2, and V3.3) and RCP 8.5 (annual average 326–327 mm) (V2.3-V2.5 and V3.3) as compared to V1 (BaU) (315 mm–RCP 4.5 and 324 mm–RCP 8.5) for the years 2041–2050. On the other hand, the lowest increase in soil water content is observed in the V3.5 variant, with an annual average of 292 mm for RCP 4.5 and an annual average of 311 mm for RCP 8.5. Thus, for future climate change scenarios, irrigation with water reservoirs (ponds and storage reservoirs) should be considered. The study proves the rationale behind building ponds in small catchments in order to increase water resources in a landscape and also to counteract adverse effects of climate changes, i.e., sediment outflow and surface water erosion. [ABSTRACT FROM AUTHOR]

The poultry sector is large and expanding in the global South, playing a central role in providing increased protein to a rapidly growing base of consumers. The sector includes small backyard farms, small commercial operations, and very large, complex enterprises. Although there is substantial literature on climate adaptation by crop farmers and large livestock farmers, such information is limited for poultry. This study focuses on the effects of higher temperatures on commercial poultry farms in southwest Nigeria and their adaptation strategies. We use a rich set of in-depth interviews to describe how poultry farmers are adapting to higher temperatures and their reasons for adopting particular practices. In general, interviewees are aware that temperatures have increased over time and that heat stress reduces poultry productivity in terms of weight gain and laying capacity. They are knowledgeable and are not passively enduring the adverse effects of higher temperatures as they have adopted a range of adaptation practices. This study identified three main adaptation strategies: (i) keeping drinking water cool, (ii) keeping the building cool and increasing ventilation, and (iii) giving birds medicines and supplements that help them cope with increased heat. Small farms tend to adopt simple and low-cost practices, and large farms tend to adopt more sophisticated and expensive approaches, in line with the nature of their respective operations. The paper's findings can help address gaps in strategies aiming to help this critically important sector of the food system be robust to future environmental change. [ABSTRACT FROM AUTHOR]

Humanity's growing appetites for food and energy are placing unprecedented yield targets on our lands. Chasing those ever‐expanding land intensification targets gave rise to monocultures and sharpened the divide between food and energy production groups. Here, we argue that this does not have to be a zero‐sum game if food and energy can be co‐generated in the same land. Co‐generation can lead to sustainable intensification but requires a paradigm shift in the way we manage our resources, particularly light. Using an extended model of plant photosynthesis and transpiration, we demonstrate how plants react to different incident light spectra and show that manipulating light could be effective for boosting land and water efficiencies, thus potentially improving soil health. This knowledge can possibly unlock the real potential of promising modern agricultural technologies that target optimization of light allocations such as agrivoltaics. This study suggests that the blue part of the light spectrum is less efficient in terms of carbon assimilation and water use and could be more effectively used to produce solar energy, while the red part could efficiently produce biomass. A sensitivity analysis to the most important crop and environmental variables (irradiance, air temperature, humidity, and CO2 concentration) shows that plant response to different light treatments is sensitive to environmental boundary conditions and is species‐specific. Therefore, further research is necessary to assess which crops and climates are more suitable to optimize the proposed food‐water‐energy nexus. Plain Language Summary: Achieving sustainability for the deeply interlinked water, energy, and food systems requires revolutionary, rather than incremental, solutions at their nexus. This includes forms of food and energy co‐generation like agrivoltaics, which can boost land and water efficiencies and improve soil health. Advancements along those lines require deep understanding of how plants react to different incident light spectra. In this paper, we demonstrate the importance of different light spectra, and show that those spectra, if optimized in terms of their utilization, could lead to sustainable and more efficient food and energy systems. For example, the red part of the light spectrum is more efficient in terms of carbon assimilation and water use while the blue part could be more effectively used to produce solar energy. Key Points: We develop a photosynthesis and transpiration model to account for different light spectraThe red part of the light spectrum is more efficient for plant photosynthesis and water useThe blue part of the light spectrum could be used for solar energy production [ABSTRACT FROM AUTHOR]

Benincasa hispida (winter melon, wax gourd or ash gourd) is a common vegetable widely grown in East and South Asia, with a very large fruit which by maturity loses its hairs and develops a waxy coating. The fruit is used in traditional medicine due to its proprieties: anti-inflammatory, demulcent, diuretic, expectorant, febrifuge, laxative and tonic, etc. The present paper presents the results of a field research regarding the cultivation of this plant in the pedoclimatic conditions of Romania. The research was carried out in an ecological experimental field from south of Romania (in the period May-October 2012) and is very important for the introduction to culture of this plant in our country. The results obtained and presented in this paper reveal the degree of adaptation to climate and soil and the physicochemical proprieties. Due to our results we consider that B. hispida is a plant that can be grown in our country with good technological and economical performances. [ABSTRACT FROM AUTHOR]

Climate is an essential element in agricultural production, and climate change inevitably have an impact on agriculture. Assessing the economic consequences of climate change requires comprehensive assessments of the impact chain from climate to crops and the economy. In our previous study, we derived a dose‐response function to estimate the response of crop yields to climate variables through a systematic review. In this paper, a dynamic multiregional input‐output model is established to assess the economic consequences of changes in agricultural production on China's regional and sectoral levels. The results show that (1) the direct economic damage is equivalent to 1% of gross domestic product (GDP) which implies the resulting economic cascade effect (ECE) that amounts to 17.8% of China's GDP. At the end of 21st century, the ECE is −0.1% to 13.6% of GDP (negative values indicate economic gains) without considering CO2 fertilization effect, of which the ECE in the most pessimistic pathway are equivalent to the total agricultural output in China today. (2) Regional‐level results show an uneven distribution of economic impact in China, which is related to the regional economic development. The least developed region in China experiences 2.8 to 8.5 times more ECE caused by climate change than the most developed region. (3) Sector‐level results show that agriculture is still the main affected sector, but in developed regions, manufacturing and services also bear part of the ECE. Plain Language Summary: Evidence from numerous studies has confirmed the impact of climate change on agriculture. This paper assesses the economic consequences of changes in agricultural production under climate change in China. We find that the direct economic damage is equivalent to 1% of gross domestic product which implies the resulting economic cascade effect that amounts to 17.8% of total gross domestic product. The most pessimistic estimate of the economic impacts in the 2090s is equivalent to China's total agricultural output today without considering CO2 fertilization effect of climate change. The economic impacts suffered by different regions of China are related to regional economic development. The least developed region in China experiences more economic damage from climate change than the most developed region. In addition to the agricultural sector, manufacturing and services are expected to experience part of the impacts, especially in developed regions. This paper hopes to provide data support for a comprehensive understanding of climate change impacts in different regions of China by assessing the economic consequences. Key Points: The economic cascading effect of climate change is approximately 18 times larger than its direct damageThe least developed region in China experiences 2.8 to 8.5 times more economic impacts than the most developed regionThe decline in agricultural output also affects manufacturing and services through trade networks, especially in developed regions [ABSTRACT FROM AUTHOR]

Miscanthus is a perennial wild plant that is vital for the production of paper and roofing, as well as horticulture and the development of new high-yielding crops in temperate climates. Chromosome-level assembly of the ancient tetraploid genome of miscanthus chromosomes is reported to provide resources that can link its chromosomes to related diploid sorghum and complex polyploid sugarcane. Analysis of Miscanthus sinensis and Miscanthus sacchariflorus showed intense mixing and interspecific hybridization and documented the origin of a high-yielding triploid bioenergetic plant, Miscanthus × giganteus. The Miscanthus genome expands comparative genomics functions to better understand the main abilities of Andropogoneae herbs. Miscanthus × giganteus is widely regarded as a promising lignocellulosic biomass crop due to its high-biomass yield, which does not emit toxic compounds into the environment, and ability to grow in depleted lands. The high production cost of lignocellulosic bioethanol limits its commercialization. The main components that inhibit the enzymatic reactions of fermentation and saccharification are lignin in the cell wall and its by-products released during the pre-treatment stage. One approach to overcoming this barrier could be to genetically modify the genes involved in lignin biosynthesis, manipulating the lignin content and composition of miscanthus. [ABSTRACT FROM AUTHOR]

This paper estimates Malaysian farmers' willingness to pay (WTP) for a planned adaptation programme for addressing climate issues in the Malaysian agricultural sector. We used the contingent valuation method (CVM) for a monetary valuation of farmers' preferences for a planned adaptation programme by ascertaining the value attached to address climatic issues in the Malaysian agricultural sector. Structured questionnaires were distributed among the sampled farmers. The study found that 74 % of respondents were willing to pay for a planned adaptation programme and that several socioeconomic and motivation factors have greater influence on their WTP. This paper clearly specifies the steps needed for all institutional bodies to better address issues in climate change. The outcomes of this paper will support policy makers to better design an efficient adaptation framework for adapting to the adverse impacts of climate change. [ABSTRACT FROM AUTHOR]

The availability of coherent time series of crop statistics is essential to better analyze the past, understand the present, and predict future trends in yield, area, and production. Importantly, such data also underpin assessments and subsequent policy actions that can shape future food systems that are more resilient in the face of climate change and sustainable in terms of resource use efficiency. In the European Union (EU), there currently is no legal obligation for EU countries to provide subnational crop statistics. Yet, such data could improve in-season crop forecasts, climate change impacts and adaption need assessments, and evaluation of agri-environmental schemes. The dataset described in this paper includes a harmonized collection of subnational crop statistics on area, production, and yield, collected for the EU from National Statistical Institutes (NSIs) and the Eurostat REGIO database -- subnational crop statistics voluntarily contributed by EU countries. The crops considered are wheat (including soft and durum wheat), barley (including winter and spring barley), grain maize, sunflower and sugar beet. All data is harmonized towards the hierarchical structure of the Eurostat legend, and the regional classification of NUTS (Nomenclature of Territorial Units for Statistics) version 2016, to provide coherent time series. A total of 344282 records is presented here (115974 for wheat, 122705 for barley, 35274 for grain maize, 34916 for sugar beet, and 35413 for sunflower) for a total of 961 regions in 27 EU countries. Statistics are reported from 1975 to 2020, with a median time spans range of 21 years. A flagging system details for each data record information on data sources, processing steps, and quality checking results. This includes consistency checks between reported values for area, yield and production, identification of null values, missing and calculated data, information on crop legend matching, and NUTS versioning.We illustrate the value of this dataset by analyzing impacts on crops and production zone shifts in Europe due to climatic and economic factors. Recommendations and future developments of collecting subnational statistics at EU level are briefly discussed. The dataset is accessible with ECAS login at https://doi.org/10.2905/685949ff-56de-4646-a8df-844b5bb5f835 (Ronchetti et al., 2023b). [ABSTRACT FROM AUTHOR]

In light of the rapidly growing agricultural sector, spotting signs of climate change is crucial. Even a slight change in climate behaviour can hinder crop development. As a result, it is necessary to investigate these climatic signals at a localised regional scale. The current methods mainly emphasise on outliers when identifying abnormalities in geographical and temporal data. This disregards the possibility of negative impacts brought on by climatic changes at each location. This paper introduces a novel mathematical approach, named the probabilistic convolution neighborhood technique (PCNT), that finds the temporal and spatial coherence at each point. Its ability to examine distribution at all grid points makes it easy to identify even a slight change in climatic variables. This new approach has been applied to study the variations in parameters, namely humidity, precipitation, mean, and maximum temperatures, that are collected from the Indian Monsoon Data Assimilation and Analysis (IMDAA) for the time period 1980 to 2020. The datasets are daily single-level datasets from which annual and seasonal (Kharif, Rabi, and Zaid) data are obtained. Spatio-temporal properties, along with inter-vicennial and inter-decadal variations of climatic variables with regions having low, high, and normal probability distributions, are studied. The findings showed a pattern in the temporal features between Kharif and Rabi. The mean temperature for the annual and seasonal data showed decreasing trends for tail diagnostics. The study brings out an understanding in the probable change in various metrics, which are crucial in the context of crop growth and agricultural practice. [ABSTRACT FROM AUTHOR]

Tamil Nadu, in southern India, has the second-largest number of neem trees in the country. The oil from the seeds has high economic significance for cottage industries in the region. This paper examines 28 Candidate Plus Trees (CPTs) selected from six agroclimatic zones in Tamil Nadu which exhibit exceptional traits such as superior growth and other desirable characteristics. We aimed to understand seed morphology variations and physicochemical properties in the oil across different regions. Significant differences were observed for morphometric traits. Fruit production correlated negatively with rainfall. 100-seed kernel weight and seed length correlated with oil percentage. Rainfall influenced seed breadth and pericarp weight. Clustering using morphological characters did not group genotypes from the same region; while soil type could distinguish them. Correlation helped us determine the prominent features which influence the traits of interest, which can be useful for breeding programs, cultivation practices, and the development of neem-based products in Tamil Nadu and beyond. [ABSTRACT FROM AUTHOR]

The existing Laspeyres index, Paasche's index, and Fisher's index under classical statistics cannot be applied when observations are indeterminate, imprecise, and uncertain. In this paper, the Laspeyres index, Paasche's index, and Fisher's index are introduced to deal with the data having neutrosophy. We present these indexes when indeterminacy is present in the data. To see the aggregate change in the production of rice crops due to climate change under an uncertain environment, data is collected from nine districts of Punjab, Pakistan. From the data and comparative studies, it is concluded that the proposed indexes are more informative, flexible, and suitable to be applied under indeterminate conditions. [ABSTRACT FROM AUTHOR]

The world’s food and agricultural programs have gradually declined into an unsustainable situation due to challenges such as increase in world population, varied agro-climatic regions, increase temperature, extreme sole-culture growing techniques, and water shortage. A considerable emphasis has been put on few staple food crops coupled with repeated dieting, food scarcity, and essential mineral deficits, frequently inducing dietary disorders. Because relying on staple crops may lead to serious food shortages in the future, we must adjust our dietary habits to include a diverse range of non-staple foods and maximize their use in order to achieve food security and reduce the nutritional gap. To assure healthy meals around the world, an authentic and reasonable strategy is presented to draw additional awareness towards variations in agricultural production techniques and dietary preferences. The EAT-Lancet declaration highlighted the importance of increasing agri-based foods to achieve sustainable health. Expanding overlooked crops with abundant genetic stocks and possibly beneficial characteristics is an approach that might meet food and nutritional security challenges. Although undervalued, pumpkin is a valuable vegetable herbaceous plant that contributes to global food and nutritional security. This crop has already been identified as a revolutionary age crop, balanced food, and more adapted to low soil and atmospheric circumstances than other major crops. This review paper focuses on the potential uses of pumpkin as an underutilized crop; diversification and development of hybrids, particularly hybridization breeding through diallel mating design; and how implementation of this “modern” technology would contribute to the breeding of the neglected pumpkin vegetable and stimulate productivity and nourish the world’s largest malnourished, deprived, and starved populations. [ABSTRACT FROM AUTHOR]

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This paper explored the status of climate-smart agriculture (CSA) in southeast Nigeria using qualitative and quantitative methods in data collection. One hundred and sixty farmers were selected from the area. Also, focus group discussions were conducted. Data collected were subjected to qualitative analysis and logit regression. The study identified five broad and important practices relevant to CSA in southeast Nigeria, which include: adjusting agricultural production systems, mobility and social networks, farm financial management, diversification on and beyond the farm, and knowledge management and regulations. The determinants of CSA in southeast Nigeria include: education, income, credit, extension, livestock ownership, farming experience, land area cultivated, distance to the market and water resources, leadership position, risk orientation, gender, land ownership, household size, and mass media exposure. Government policies need to support research and development that develops and diffuses the climate-smart technologies to help farmers respond changes in climatic conditions. [ABSTRACT FROM AUTHOR]

The adaptation measures of agricultural crops to current climate change are also reflected at the level of land-use planning for species with resistance to rainfall shortages and periods of prolonged droughts. The present paper aimed at the spatial modeling of land favorability for alfalfa (Medicago sativa L.) within Bihor county, which is located in Northwest Romania. This was performed by using GIS spatial analysis techniques, particularly the technique of rating agricultural plots by using as a modeling basis the synthetic climate indicators, soil indices, as well as topographic characteristics, which are considered relevant for the studied species. As a result of the modeling process, agricultural plots that are favorable for the alfalfa culture were identified. A comparative analysis of the profit was also carried out both for the green and dry masses of eight types of alfalfa, which are suitable for the national environmental conditions. Land favorability modeling for alfalfa cultivation will be carried out by using GIS spatial analysis techniques, which allow the use of large databases while maintaining the necessary degree of detail, and they make possible the correlations between factors and production estimated per hectare, which are correlations that are characteristic for the environmental factors. Thus, for the studied territory, the best green alfalfa production was identified as being obtained from the Carinca and Daniela varieties, whereas the Mihaela and Teodora varieties were noticed to provide the best dry alfalfa production. [ABSTRACT FROM AUTHOR]

Copyright of Historia Agraria is the property of Historia Agraria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of this paper was to determine the mechanisms of climate change impact on the yield of the main exportoriented crops in the agro-climatic zones of Ukraine. The study of the problem of changing the acreage of the main export-oriented crops was conducted according to the data of the State Statistics Service of Ukraine on the time horizon 2000-2018 in the following order: first, the dynamics of the change of the acreage under corn, sunflower and wheat by the agro-climatic zones of Ukraine was analyzed; secondly, the trends of yield changes of these crops were investigated based on the increase in the difference in yields between the northern and southern zones; and, finally, the temporal and spatial expansion in the area of crop propagation were investigated by applying the panel regression method. The findings obtained indicate that the applied models confirm the assumption of the effects of climate change on crop yield changes and the zones expansion in the northern direction. If the country’s wheat area can be considered stable (variation is insignificant), then the corn and sunflower areas have grown steadily under the influence of increasing demand from national and world markets. At the same time, the growing acreage under corn and sunflower occurred in all climatic zones. Stable expansion of corn crops in the north direction in all three agroclimatic zones of Ukraine has been statistically confirmed. The article presents the findings of empirical analysis, which confirm that if the boundaries of soil and climatic zones change, the conditions of growing crops and their yield will consequently change as well. Thus, based on current global forecasts, the impact of weather on Ukraine’s agriculture will increase, and the most negative effects can be expected in the Steppe zone, where the likelihood of weather and climate risks increases, requiring the development of adaptation and mitigation measures as well as exploitation of new potential opportunities that are being opened. Studies have shown that there is an expansion in crops to the north and a change in their pattern, including a significant increase in the area under corn. [ABSTRACT FROM AUTHOR]

Climate change and its impacts on the physical environment have received increased attention in recent years, spurring debates on its global and local dimensions. While a common ground has been reached on its causes, manifestations and options for mitigation, its human security implications remain poorly understood. Links between climate change and social conflict is one of the most controversial issues in the climate change impacts assessment literature as priorities diverge between generalised scientific validity (pedagogy) and context-based security analysis (policy). This paper examines climate change-conflict linkages drawing upon experiences of migrant pastoralists and arable farming host communities in Nigeria. It found a strong causal linkage between exposure to impacts of climate change and growing incidence of conflict over renewable natural resources. It recommends contextualised analyses of the linkage as well as the integration of climate-related conflict into Nigeria's security policy frameworks particularly, its climate change impact assessment and intervention strategies. [ABSTRACT FROM AUTHOR]

Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn. [ABSTRACT FROM AUTHOR]

The organic sector in our country, although as shown in continuous development, faces a multitude of problems: the climatic conditions of our country, characterized by periods of drought in many parts of the country, high input prices, the majority of which are imported; difficulties in identifying markets for products, reduced subsidies, standardized conditions difficult to meet, etc. The problems the sector is facing reflect in the organization of the production activity and hence the economic performance of farm production. Accordingly, the aim of this paper was to analyze on the basis of annual financial and accounting information collected in the two vegetable farms and the two animal breeding farms, their efficiency / inefficiency, and the results were compared to identify the causes of the differences obtained in the efficiency at a farm level. The results obtained reveal a higher level of return on integrated vegetable farm in a joint recovery and a high efficiency for chain int [ABSTRACT FROM AUTHOR]

As regards to organic farming, organic farms have a lot of shortcomings in ensuring smooth organization of production due to climatic factors or crop sensitivity and action of pests and diseases, but especially to the high cost of inputs, reduced subsidies and difficulties in obtaining fair prices on the market. Understanding how the organizational structure of the business can compete to ensure efficiency at farm level is an important means to resolve these deficiencies. In this context, this paper aims to identify the characteristics of the organization of organic crop farms starting from an interview-based analysis of two large crop specialised farms in Tulcea and Calaraşi Counties. The information obtained through this method of investigation has been translated into a SWOT analysis and represented the basis for comparison with information gathered from other interviews from two organic farms in Scotland. The main conclusions we reached highlight two types of organization systems, one without integration and another with supply chain integration, very similar to the Scottish ones, but also showing a very obvious difference in the mentality of the farm owners; Romanians focusing on meeting the conditions for certification and maintenance of crops in organic, and the Scots at finding new markets. [ABSTRACT FROM AUTHOR]

Land-surface models (LSMs) typically simulate a single crop per year in a field or location. However, actual cropping systems are characterized by a succession of distinct crop cycles that are sometimes interspersed with long periods of bare soil. Sequential cropping (also known as multiple or double cropping) is particularly common in tropical regions, where the crop seasons are largely dictated by the main wet season. In this paper, we implement sequential cropping in a branch of the Joint UK Land Environment Simulator (JULES) and demonstrate its use at sites in France and India. We simulate all the crops grown within a year in a field or location in a seamless way to understand how sequential cropping influences the surface fluxes of a land-surface model. We evaluate JULES with sequential cropping in Avignon, France, providing over 15 years of continuous flux observations (a point simulation). We apply JULES with sequential cropping to simulate the rice–wheat rotation in a regional 25 km resolution gridded simulation for the northern Indian states of Uttar Pradesh and Bihar and four single-grid-box simulations across these states, where each simulation is a 25 km grid box. The inclusion of a secondary crop in JULES using the sequential cropping method presented does not change the crop growth or development of the primary crop. During the secondary crop growing period, the carbon and energy fluxes for Avignon and India are modified; they are largely unchanged for the primary crop growing period. For India, the inclusion of a secondary crop using this sequential cropping method affects the available soil moisture in the top 1.0 m throughout the year, with larger fluctuations in sequential crops compared with single-crop simulations even outside the secondary crop growing period. JULES simulates sequential cropping in Avignon, the four India locations and the regional run, representing both crops within one growing season in each of the crop rotations presented. This development is a step forward in the ability of JULES to simulate crops in tropical regions where this cropping system is already prevalent. It also provides the opportunity to assess the potential for other regions to implement sequential cropping as an adaptation to climate change. [ABSTRACT FROM AUTHOR]

Ammonia emissions into the atmosphere have increased substantially in Europe since 1960, primarily due to the intensification of agriculture, as illustrated by enhanced livestock and the use of fertilizers. These associated emissions of reactive nitrogen, particulate matter, and acid deposition have contributed to negative societal impacts on human health and terrestrial ecosystems. Due to the limited availability of reliable measurements, emission inventories are used to assess large-scale ammonia emissions from agriculture by creating gridded annual emission maps and emission time profiles globally and regionally. The modeled emissions are subsequently utilized in chemistry transport models to obtain ammonia concentrations and depositions. However, current emission inventories usually have relatively low spatial resolutions and coarse categorizations that do not distinguish between fertilization on various crops, grazing, animal housing, and manure storage in its spatial allocation. Furthermore, in assessing the seasonal variation of ammonia emissions, they do not consider local climatology and agricultural management, which limits the capability to reproduce observed spatial and seasonal variations in the ammonia concentrations. This paper describes a novel ammonia emission model that quantifies agricultural emissions with improved spatial details and temporal dynamics in 2010 in Germany and Benelux. The spatial allocation was achieved by embedding the agricultural emission model Integrated Nitrogen Tool across Europe for Greenhouse gases and Ammonia Targeted to Operational Responses (INTEGRATOR) into the air pollution inventory Monitoring Atmospheric Composition and Climate-III (MACC-III), thus accounting for differentiation in ammonia emissions from manure and fertilizer application, grazing, animal houses and manure storage systems. The more detailed temporal distribution came from the integration of TIMELINES, which provided predictions of the timing of key agricultural operations, including the day of fertilization across Europe. The emission maps and time profiles were imported into LOTOS-EUROS to obtain surface concentrations and total columns for validation. The comparison of surface concentration between modeled output and in situ measurements illustrated that the updated model had been improved significantly with respect to the temporal variation of ammonia emission, and its performance was more stable and robust. The comparison of total columns between remote sensing observations and model simulations showed that some spatial characteristics were smoothened. Also, there was an overestimation in southern Germany and underestimation in northern Germany. The results suggested that updating ammonia emission fractions and accounting for manure transport are the direction for further improvement, and detailed land use is needed to increase the spatial resolution of spatial allocation in ammonia emission modeling. [ABSTRACT FROM AUTHOR]

Coupled atmosphere-ocean general circulation models (GCMs) simulate different realizations of possible future climates at global scale under contrasting scenarios of land-use and greenhouse gas emissions. Such data require several additional processing steps before it can be used to drive impact models. Spatial downscaling, typically by regional climate models (RCM), and bias-correction are two such steps that have already been addressed for Europe. Yet, the errors in resulting daily meteorological variables may be too large for specific model applications. Crop simulation models are particularly sensitive to these inconsistencies and thus require further processing of GCM-RCM outputs. Moreover, crop models are often run in a stochastic manner by using various plausible weather time series (often generated using stochastic weather generators) to represent climate time scale for a period of interest (e.g. 2000 ± 15 years), while GCM simulations typically provide a single time series for a given emission scenario. To inform agricultural policy-making, data on near- and medium-term decadal time scale is mostly requested, e.g. 2020 or 2030. Taking a sample of multiple years from these unique time series to represent time horizons in the near future is particularly problematic because selecting overlapping years may lead to spurious trends, creating artefacts in the results of the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data for Europe that addresses these problems. Input data consist of daily temperature and precipitation from three dynamically downscaled and bias-corrected regional climate simulations of the IPCC A1B emission scenario created within the ENSEMBLES project. Solar radiation is estimated from temperature based on an auto-calibration procedure. Wind speed and relative air humidity are collected from historical series. From these variables, reference evapotranspiration and vapour pressure deficit are estimated ensuring consistency within daily records. The weather generator ClimGen is then used to create 30 synthetic years of all variables to characterize the time horizons of 2000, 2020 and 2030, which can readily be used for crop modelling studies. [ABSTRACT FROM AUTHOR]

CropWatch agroclimatic indicators (CWAIs) are a monitoring tool developed by the CropWatch global crop monitoring system in the Chinese Academy of Sciences (CAS; , Wu et al Int J Digital Earth 7(2):113-137, 2014, Wu et al Remote Sens 7:3907-3933, 2015). Contrary to most other environmental and agroclimatic indicators, they are 'agronomic value-added', i.e. they are spatial values averaged over agricultural areas only and they include a weighting that enhances the contribution of the areas with the largest production potential. CWAIs can be computed for any time interval (starting from dekads) and yield one synthetic value per variable over a specific area and time interval, for instance a national annual value. Therefore, they are very compatible with socio-economic and other variables that are usually reported at regular time intervals over administrative units, such as national environmental or agricultural statistics. Two of the CWAIs are satellite-based (RAIN and Photosynthetically Active radiation, PAR) while the third is ground based (TEMP, air temperature); capitals are used when specifically referring to CWAIs rather than the climate variables in general. The paper first provides an overview of some common agroclimatic indicators, describing their procedural, systemic and normative features in subsequent sections, following the terminology of Binder et al Environ Impact Assess Rev 30:71-81 (2010). The discussion focuses on the systemic and normative aspects: the CWAIs are assessed in terms of their coherent description of the agroclimatic crop environment, at different spatial scales (systemic). The final section shows that the CWAIs retain key statistical properties of the underlying climate variables and that they can be compared to a reference value and used as monitoring and early warning variables (normative). [ABSTRACT FROM AUTHOR]

Agriculture in South Asia is vulnerable to climate change. Therefore, adaptation measures are required to sustain agricultural productivity, to reduce vulnerability, and to enhance the resilience of the agricultural system to climate change. There are many adaptation practices in the production systems that have been proposed and tested for minimizing the effects of climate change. Some socioeconomic and political setup contributes to adaptation, while others may inhibit it. This paper presents a systematic review of the impacts of climate change on crop production and also the major options in the agricultural sector that are available for adaptation to climate change. One of the key conclusions is that agricultural practices that help climate change adaptation in agriculture are available, while the institutional setup to implement and disseminate those technical solutions is yet to be strengthened. Thus, it is important to examine how to bring the required institutional change, generate fund to invest on these changes, and design dynamic policies for long-term climate change adaptation in agriculture rather than a mere focus on agricultural technology. This is one of the areas where South Asian climate policies require reconsidering to avoid possible maladaptation in the long run. [ABSTRACT FROM AUTHOR]

The rapid development of internet, cloud computing and sensor networks lead to develop and deploy the Internet of Things (IoT) which is a hot topic for the researchers. It has started to be used in various areas. Thus, agriculture is one of the most popular IoT research area. In agriculture environment, farming platform area is being a huge open structure and farmers must protect the crops from extreme weather conditions namely; wind speed/direction, precipitation, air temperature, solar radiations, and relative humidity etc. These extreme weather conditions effect crops and farms very significantly. But with the benefits of Internet of Things technologies, an agriculture business become more easy and efficient despite extreme weather conditions. This paper provides a model of smart agriculture environment using neural networks that helps the farmers to make more accurate predictions for the future according to weather conditions. This paper proposed a time-delay radial basis function (TDRBF) network approach to model temporal and sequential relationship between the various weather condition sensor readings from the agricultural environment. The performance of the acquired network model was analysed statistically and presented in this paper. As a result, the results of the neural network model show that it could be used to predict the desired weather condition sensor readings beforehand in order to increase the productivity in agricultural environment and also it is possible that by using such an intelligent learning system could provide a life-long learning for the changing weather conditions in the farming area over the years. [ABSTRACT FROM AUTHOR]

Motivation: Knowledge on the origins of food crops in the wild is abundant but fragmentary and unconsolidated, which hinders macroecological approaches to long‐standing questions on the phylogeography and history of crop origins. Main types of variable contained: (a) Comprehensive checklist of crop species cultivated for food; (b) data on diverse continuous and categorical descriptors of antiquity of cultivation (earliest written records of cultivation and of domestication), organ harvested for primary use, and growth form; (c) identities, geographic distribution and current climate at the origin of crops' wild progenitors; (d) a time‐calibrated molecular phylogeny (Phylo Food v 1.0) of food crops. Spatial location and grain: Global. Spatial accuracy of geographic records for wild progenitors varies among Global Biodiversity Information Facility (GBIF) source databases. WorldClim data were retrieved at 2.5 min (approximately 4.5 km at the equator) spatial resolution. Time period and grain: Temporal accuracy of estimation of cultivation antiquity varies among sources, but unit of measurements is years ago. The scale of the phylogenetic tree is millions of years ago. Major taxa and level of measurement: Flowering plants. Specifically, 866 species of food crops and 901 species of wild progenitors. Most information was retrieved at the species level. However, geographic occurrence of wild progenitors was obtained from GBIF records of observations of individuals of a given species in the field. Software format: Crop Origins v 1.0 and Phylo Food v 1.0 are provided as zipped files in the Supporting Information of this paper. The zipped files contain a. csv file with v 1.0 of the Crop Origins database, a. tre file with Newick format as Phylo Food v 1.0, metadata and literature sources files for both resources, and R code used to build Phylo Food v 1.0. The resources are also deposited, and will be regularly updated, at https://rubenmilla.github.io/Crop_Origins_Phylo. [ABSTRACT FROM AUTHOR]

Cold- and heat-related mortality poses significant public health concerns worldwide. Although there are numerous studies dealing with the association between extreme ambient temperature and mortality, only a small number adopt a synoptic climatological approach in order to understand the nature of weather systems that precipitate increases in cold- or heat-related mortality. In this paper, the Lamb Weather Type synoptic classification is used to examine the relationship between daily mortality and weather patterns across nine regions of England. Analysis results revealed that the population in England is more susceptible to cold weather. Furthermore, it was found that the Easterly weather types are the most hazardous for public health all-year-long; however, during the cold period, the results are more evident and spatially homogenous. Nevertheless, it is noteworthy that the most dangerous weather conditions are not always associated with extreme (high or low) temperatures, a finding which points to the complexity of weather-related health effects and highlights the importance of a synoptic climatological approach in elucidating the relationship between temperature and mortality. [ABSTRACT FROM AUTHOR]

Establishing linkages between Meteorological and climatic data, and farming decision-making is a challenging task. The following paper addresses the challenges associated with this. A large amount of weather and climate information is presently available for farmers. A portion of the information is operational or already under development, and in particular, forecasting through climatic data and formation may not be suitable for farmers when it comes to the decision-making process. The best way to gain an advantage from natural factors is to consider them during decision-making and understand them in the best way possible. Meteorological information pertaining to agriculture, and climatic data, in particular, is an important aspect of planning in the context of agricultural production. Therefore, climatic conditions must be an integral part of the decision-making process. These factors can be determined by recording hourly, daily, and weekly temperature data, rainfall, solar radiation, wind speed, evaporation, relative humidity, and evapotranspiration. Artificial Neural Networks possess the capability of not just analysing the data but also learning from the data. This paper presents a predictive analysis to analyse the best crop which can be produced for specific weather conditions and also suggests a hybrid recommender system that adopts CBR - Case-Based Reasoning for enhancing the success ratio of the system. This proposed novel hybrid system is a combination of the collaborative filtering technique and case-based reasoning.The novelty of the model lies in the of district-wise agriculture data analysis for predicting future climatic conditions and recommending crops based on that climatic conditions and also considering the agriculture pattern of the district using a hybrid recommender system. [ABSTRACT FROM AUTHOR]

This study looked at socio-economic factors that affect how farmers of arable crops adapt to climate variability. Using a multistage sample strategy, a structured interview was conducted to gather information from 150 farmers in rural communities across the local governments in the study area. Descriptive and inferential statistics, including chi-square and Pearson Product Moment Correlation, were utilized to analyse the study's data. The research showed that farmers’ socio-economic characteristics influence adaptation strategies. Household size (χ2=179.3), and farm size (χ2=136.4) positively influence adaptation strategies to be adopted by farmers at p<0.05. Additionally, there was a strong correlation between farmers' perceptions of climate variability and adaptation strategies (r=0.591, p<0.05). According to the study's findings, farmers' perceptions of climate unpredictability and socio-economic characteristics have a significant impact on their adaptation strategies. The study recommended that climate variability awareness and sensitization should be put in place at the Local, State, and Federal government level to have a community whereby the farmers are adeptly aware of the causes and effects of climate variability in the area. [ABSTRACT FROM AUTHOR]

Copyright of La Granja, de Ciencias de la Vida is the property of Universidad Politecnica Salesiana and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The crops are vulnerable to variations in the annual climate, therefore, reducing losses and increasing the agricultural production are necessary, both economically and socially. The purpose of this paper is to analyze the thermic and hydric resources and to indentify the agro-climatic parameters and critical thresholds on the intervals of period September 2006 - August 2007, with direct consequences for the winter wheat crops in the Caracal Plain. In the first decade of the 21st century, the crop year 2006-2007, was characterized, in general, by a higher air temperature than normal, with soil humidity deficit, which determined the depreciation of the vegetation state of the winter wheat crop and consequently, a low production per hectare. By knowing the climatic conditions, for a period of time, it provides the ability of implementing effective measures in order to prevent and combat effects on agricultural production. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are often built upon the "niche conservatism" assumption, such that they ignore the possibility of "evolutionary rescue" and may underestimate species' future range limits under climate change. We select aphids and ladybirds as model species and develop an eco‐evolutionary model to explore evolutionary rescue in a predator–prey system under climate change. We model the adaptive change of species' thermal performances, accounting for biotic interactions. Our study suggests that, without considering evolutionary adaptation, the warming climate will result in a reduction in aphid populations and the extinction of ladybirds in large parts of the United States. However, when incorporating evolutionary adaptation into the model, aphids can adapt to climate change, whereas ladybirds demonstrate geographic variation in their evolutionary rescue potential. Specifically, ladybirds in southern regions are more likely to be rescued than those in the north. In certain northern regions, ladybirds do not avoid extinction due to severe warming trends and seasonality of the climate. While higher warming trends do prompt stronger evolutionary changes in phenotype, they also lead to reduced aphid population abundance such that ecology constrains ladybird population growth. Higher seasonality induces an ecological effect by limiting the length of reproductive season, thereby reducing the capacity for evolutionary rescue. Together, these findings reveal the complex interplay between ecological and evolutionary dynamics in the context of evolutionary adaptation to climate change. [ABSTRACT FROM AUTHOR]