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Ruixue Wang, Roderick M. Rejesus, Jesse B. Tack, Joseph V. Balagtas, Andy D. Nelson
American Journal of Agricultural Economics; doi:10.1111/ajae.12210

Abstract:
This study examines the relationship between yields of modern rice varieties and warming temperatures. Data from a long‐running farm‐level survey in the Philippines, with rich information on planted rice varieties, allow us to estimate fixed effect econometric models of rice yields. We find that increases in temperature, especially minimum temperatures, have statistically significant negative impacts on rice yields. Point estimates of the marginal effect of higher temperatures on rice yields indicate that early modern varieties bred primarily for higher yields, pest resistance, and/or grain quality traits (i.e., not necessarily abiotic stress tolerance) tend to be more resilient to heat events than traditional rice varieties. Moreover, the marginal effect point estimates also suggest that more recent rice varieties bred for better tolerance to abiotic stresses are likely more resilient to warming than both traditional varieties and early modern varieties. Notwithstanding the heat resilience pattern suggested by these point estimates, we are unable to find statistically significant differences in the marginal yield response to warming across these three rice varietal groups. These results provide suggestive evidence that rice breeding efforts have improved resilience to warming temperatures and point to several interesting future research directions.
Published: 1 March 2021
Remote Sensing, Volume 13; doi:10.3390/rs13050921

Abstract:
Rice is the primary staple food of more than half of the world’s population and plays an especially important role in global economy, food security, water use, and climate change. The usefulness of Synthetic Aperture Radars (SAR) for rice mapping and monitoring has been demonstrated locally in many studies, in particular in the last five years with the availability of an unprecedented amount of free Sentinel-1 data within the Copernicus program. However, although earlier studies from the 1990s have laid the foundations of the physical understanding of the SAR response of rice fields, the more recent studies tend to overlook this aspect and to favor instead approaches driven by supervised learning which provide accurate results locally but cannot necessarily be extended to wide areas. The objective of this study is to analyze in detail the backscatter temporal variation of rice fields, using Sentinel-1 from 2015 to 2020 and in-situ data for the 5 rice seasons over 2 years 2017–2018, in order to derive robust SAR-based indicators useful for rice monitoring applications, which are essential for planning, monitoring and food security applications. The test region is the An Giang province, in the Mekong River Delta, Vietnam, one of the world’s major rice regions which presents a diversity in rice cultivation practices, in cropping density, and in crop calendar. The SAR data have been analyzed as a function of rice parameters, and the temporal and polarization behaviors of the radar backscatter of different rice varieties have been interpreted physically. New backscatter indicators for the detection of rice paddy area, the estimation of the sowing date, phenological stage and the mapping of the short cycle and long cycle rice varieties have been developed and discussed regarding the generality of the methods with respect to the rice cultural practices and the SAR data characteristics.
Yared Assefa, , Manoranjan K. Mondal, Jayanta Bhattacharya, Rokhsana Parvin, Shilpi R. Sarker, Mahabubur Rahman, Asish Sutradhar, P.V. Vara Prasad, Humnath Bhandari, et al.
Published: 1 February 2021
Agricultural Systems, Volume 187; doi:10.1016/j.agsy.2020.102986

The publisher has not yet granted permission to display this abstract.
, M. Golam Mahboob, Afm Tariqul Islam, Mohammed Mainuddin, Yingying Yu, Mobin D. Ahmad, Khandakar F. Ibn Murad, Kowshik K. Saha, Akbar Hossain, M. Moniruzzaman, et al.
Remote Sensing Applications: Society and Environment, Volume 21; doi:10.1016/j.rsase.2020.100460

The publisher has not yet granted permission to display this abstract.
, Akbar Hossain, Mutiu Abolanle Busari, Ram Swaroop Meena
Agroecological Footprints Management for Sustainable Food System pp 273-308; doi:10.1007/978-981-15-9496-0_9

The publisher has not yet granted permission to display this abstract.
Xiaobo Wang, , Xia Li, Bin Chen, Junbang Wang, Mei Huang, Atiq Rahman
Published: 15 November 2020
Agricultural and Forest Meteorology, Volume 294; doi:10.1016/j.agrformet.2020.108135

The publisher has not yet granted permission to display this abstract.
Punyaporn Prangbang, Kazuyuki Yagi, Jorrel Khalil S. Aunario, Bjoern Ole Sander, Reiner Wassmann, Thomas Jäkel, Chitnucha Buddaboon, Amnat Chidthaisong, Sirintornthep Towprayoon
Frontiers in Sustainable Food Systems, Volume 4; doi:10.3389/fsufs.2020.575823

Abstract:
The alternate wetting and drying (AWD) water management technique has been identified as one of the most promising options for mitigating methane (CH4) emissions from rice cultivation. By its nature, however, this option is limited only to paddy fields where farmers have sustained access to irrigation water. In addition, large amounts of rainfall often make it difficult to drain water from paddy fields. Therefore, it is necessary to understand the specific conditions and suitability of an area in which AWD is foreseen to be applied before its CH4 mitigation potential can be assessed in view of planning regional and national mitigation actions. In this study, we applied a methodology developed for assessing the climatic suitability of AWD to paddy fields in the central plain of Thailand in order to determine the potential spatial and temporal boundaries given by climatic and soil parameters that could impact on the applicability of AWD. Related to this, we also assessed the CH4 mitigation potential in the target provinces. Results showed that the entire area of the six target provinces was climatically suitable for AWD in both the major (wet) and second (dry) rice seasons. A sensitivity analysis accounting for uncertainties in soil percolation and suitability classification indicated that these settings did not affect the results of the suitability assessment, although they changed to some extent the distribution of moderate and high climatic suitability areas in the major rice season. Following the methodologies of the Intergovernmental Panel on Climate Change Guidelines, we estimated that the AWD scenario could reduce annual CH4 emissions by 32% compared with the emissions in the baseline (continuously flooded) scenario. The potential of AWD for annual CH4 emission reduction was estimated to be 57,600 t CH4 year−1, equivalent to 1.61 Mt CO2-eq year−1, in the target provinces. However, we recognize the possibility that other parameters not included in our current approach may significantly influence the suitability of AWD and thus propose areas for further improvement derived from these limitations. All in all, our results will be instrumental in guiding practitioners at all levels involved in water management for rice cultivation.
, Jan Philipp Dietrich, Felix T. Portmann, Stefan Siebert, Philip K. Thornton, , Mario Herrero
Published: 1 September 2020
Global Environmental Change, Volume 64; doi:10.1016/j.gloenvcha.2020.102131

Abstract:
Multiple cropping, defined as harvesting more than once a year, is a widespread land management strategy in tropical and subtropical agriculture. It is a way of intensifying agricultural production and diversifying the crop mix for economic and environmental benefits. Here we present the first global gridded data set of multiple cropping systems and quantify the physical area of more than 200 systems, the global multiple cropping area and the potential for increasing cropping intensity. We use national and sub-national data on monthly crop-specific growing areas around the year 2000 (1998–2002) for 26 crop groups, global cropland extent and crop harvested areas to identify sequential cropping systems of two or three crops with non-overlapping growing seasons. We find multiple cropping systems on 135 million hectares (12% of global cropland) with 85 million hectares in irrigated agriculture. 34%, 13% and 10% of the rice, wheat and maize area, respectively are under multiple cropping, demonstrating the importance of such cropping systems for cereal production. Harvesting currently single cropped areas a second time could increase global harvested areas by 87–395 million hectares, which is about 45% lower than previous estimates. Some scenarios of intensification indicate that it could be enough land to avoid expanding physical cropland into other land uses but attainable intensification will depend on the local context and the crop yields attainable in the second cycle and its related environmental costs.
Published: 18 August 2020
Remote Sensing, Volume 12; doi:10.3390/rs12162655

Abstract:
The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel-1 program expands studies of radar data (C-band) for rice monitoring at regional scales, due to the high temporal resolution and free data distribution. Recurrent Neural Network (RNN) model has reached state-of-the-art in the pattern recognition of time-sequenced data, obtaining a significant advantage at crop classification on the remote sensing images. One of the most used approaches in the RNN model is the Long Short-Term Memory (LSTM) model and its improvements, such as Bidirectional LSTM (Bi-LSTM). Bi-LSTM models are more effective as their output depends on the previous and the next segment, in contrast to the unidirectional LSTM models. The present research aims to map rice crops from Sentinel-1 time series (band C) using LSTM and Bi-LSTM models in West Rio Grande do Sul (Brazil). We compared the results with traditional Machine Learning techniques: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Normal Bayes (NB). The developed methodology can be subdivided into the following steps: (a) acquisition of the Sentinel time series over two years; (b) data pre-processing and minimizing noise from 3D spatial-temporal filters and smoothing with Savitzky-Golay filter; (c) time series classification procedures; (d) accuracy analysis and comparison among the methods. The results show high overall accuracy and Kappa (>97% for all methods and metrics). Bi-LSTM was the best model, presenting statistical differences in the McNemar test with a significance of 0.05. However, LSTM and Traditional Machine Learning models also achieved high accuracy values. The study establishes an adequate methodology for mapping the rice crops in West Rio Grande do Sul.
Eeswaran Rasu, Rachael Bernstein
Published: 18 June 2020
by Wiley
CSA News, Volume 65, pp 42-46; doi:10.1002/csan.20204

Rafal M. Gutaker, , Emily S. Bellis, , , , Emma R. Slayton, , Cristina C. Castillo, Sónia Negrão, et al.
Nature Plants, Volume 6, pp 492-502; doi:10.1038/s41477-020-0659-6

The publisher has not yet granted permission to display this abstract.
, Erik H. Murchie, UK Rice Research Community
Published: 1 May 2020
Trends in Plant Science, Volume 25, pp 421-422; doi:10.1016/j.tplants.2020.02.014

The publisher has not yet granted permission to display this abstract.
, Efisio Solazzo, Ganlin Huang, Diego Guizzardi, Ernest Koffi, Marilena Muntean, Christian Schieberle, Rainer Friedrich,
Scientific Data, Volume 7, pp 1-17; doi:10.1038/s41597-020-0462-2

Abstract:
Emissions into the atmosphere from human activities show marked temporal variations, from inter-annual to hourly levels. The consolidated practice of calculating yearly emissions follows the same temporal allocation of the underlying annual statistics. However, yearly emissions might not reflect heavy pollution episodes, seasonal trends, or any time-dependant atmospheric process. This study develops high-time resolution profiles for air pollutants and greenhouse gases co- emitted by anthropogenic sources in support of atmospheric modelling, Earth observation communities and decision makers. The key novelties of the Emissions Database for Global Atmospheric Research (EDGAR) temporal profiles are the development of (i) country/region- and sector- specific yearly profiles for all sources, (ii) time dependent yearly profiles for sources with inter-annual variability of their seasonal pattern, (iii) country- specific weekly and daily profiles to represent hourly emissions, (iv) a flexible system to compute hourly emissions including input from different users. This work creates a harmonized emission temporal distribution to be applied to any emission database as input for atmospheric models, thus promoting homogeneity in inter-comparison exercises.
Published: 16 April 2020
Sustainability, Volume 12; doi:10.3390/su12083227

Abstract:
It is necessary to develop a sustainable food production system to ensure future food security around the globe. Cropping intensity and sowing month are two essential parameters for analyzing the food–water–climate tradeoff as food sustainability indicators. This study presents a global-scale analysis of cropping intensity and sowing month from 2000 to 2015, divided into three groups of years. The study methodology integrates the satellite-derived normalized vegetation index (NDVI) of 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) and daily land-surface-water coverage (LSWC) data obtained from The Advanced Microwave Scanning Radiometer (AMSR-E/2) in 1-km aggregate pixel resolution. A fast Fourier transform was applied to normalize the MODIS NDVI time-series data. By using advanced methods with intensive optic and microwave time-series data, this study set out to anticipate potential dynamic changes in global cropland activity over 15 years representing the Millennium Development Goal period. These products are the first global datasets that provide information on crop activities in 15-year data derived from optic and microwave satellite data. The results show that in 2000–2005, the total global double-crop intensity was 7.1 million km2, which increased to 8.3 million km2 in 2006–2010, and then to approximately 8.6 million km2 in 2011–2015. In the same periods, global triple-crop agriculture showed a rapid positive growth from 0.73 to 1.12 and then 1.28 million km2, respectively. The results show that Asia dominated double- and triple-crop growth, while showcasing the expansion of single-cropping area in Africa. The finer spatial resolution, combined with a long-term global analysis, means that this methodology has the potential to be applied in several sustainability studies, from global- to local-level perspectives.
Thiago Machado Pasin, Paula Zaghetto De Almeida, Ana Sílvia De Almeida Scarcella, Juliana Da Conceição Infante,
Biorefinery of Alternative Resources: Targeting Green Fuels and Platform Chemicals pp 23-47; doi:10.1007/978-981-15-1804-1_2

The publisher has not yet granted permission to display this abstract.
Marcelo Negrini, Elisangela Gomes Fidelis, Marcelo Coutinho Picanço,
Experimental and Applied Acarology, Volume 80, pp 445-461; doi:10.1007/s10493-020-00474-6

The publisher has not yet granted permission to display this abstract.
Hiroaki Samejima, Keisuke Katsura, Mayumi Kikuta, Symon Mugo Njinju, John Munji Kimani, Akira Yamauchi,
Published: 13 February 2020
Plant Production Science, Volume 23, pp 297-305; doi:10.1080/1343943x.2020.1727752

Abstract:
Cropping calendar optimization contributes to an increase in rice yield. Information on the seasonal variation in grain yield and climate conditions is necessary to determine an appropriate cropping calendar. We sought to find the optimal cropping calendar in Mwea, Kenya, in a tropical highland in equatorial East Africa. We conducted a series of 58 experiments using a local popular rice variety, Basmati 370, between 2013 and 2016, using a secured water supply and adequate blast control, sowing every 15 days. The grain yield was 0–2 t ha−1 when the variety was sown between March and June. This poor grain yield was attributable to the low temperature and low solar radiation from May to September. In contrast, the grain yield was always more than 3 t ha−1 when the variety was sown between July and February. Sowing Basmati 370 between March and June is not recommended, because it may lead to a suboptimal yield due to cold stress. The current cropping calendar (July–December or August–January) is acceptable even under abundant year-round water supply, but sowing between October and February is a good alternative sowing period for single rice cropping. Rice production per year is expected to increase to >100% with the introduction of double cropping by adding cultivation from between January and February before the current cropping calendar. These findings serve as useful references for considering and determining the appropriate calendar options for single and double cropping of rice in tropical highlands in equatorial East Africa. Graphical Abstract
Geli Zhang, , , Fengfei Xin, , , , Berrien Moore
Nature Communications, Volume 11, pp 1-11; doi:10.1038/s41467-019-14155-5

Abstract:
Agriculture (e.g., rice paddies) has been considered one of the main emission sources responsible for the sudden rise of atmospheric methane concentration (XCH4) since 2007, but remains debated. Here we use satellite-based rice paddy and XCH4 data to investigate the spatial–temporal relationships between rice paddy area, rice plant growth, and XCH4 in monsoon Asia, which accounts for ~87% of the global rice area. We find strong spatial consistencies between rice paddy area and XCH4 and seasonal consistencies between rice plant growth and XCH4. Our results also show a decreasing trend in rice paddy area in monsoon Asia since 2007, which suggests that the change in rice paddy area could not be one of the major drivers for the renewed XCH4 growth, thus other sources and sinks should be further investigated. Our findings highlight the importance of satellite-based paddy rice datasets in understanding the spatial–temporal dynamics of XCH4 in monsoon Asia.
, David Šebela, Cherryl Quiñones,
Published: 1 January 2020
by Wiley
Crop Science, Volume 60, pp 391-403; doi:10.1002/csc2.20086

Abstract:
High night‐temperature (HNT) stress during the reproductive stage of rice (Oryza sativa L.) reduces spikelet fertility and yield by inhibiting important physiological processes. However, specifics such as the period of time that is most sensitive to HNT, is unknown. To investigate this, we conducted four controlled‐environment experiments with two rice cultivars, N22 (HNT tolerant) and WAB56–104 (HNT susceptible). These cultivars were exposed to varying durations and intensities of night temperatures (control, 24°C; HNT, 30 and 35°C) during the reproductive stage. The effect of HNT on spikelet fertility and grain weight varied with duration: spikelet fertility reduced by 47–77% when exposed to HNT for 15 nights, 6–29% when exposed for four nights, and 9–15% when exposed for 5.5 h (pre‐midnight, 1830–0000 h or post‐midnight, 0000–0530 h) for four nights. Spikelet fertility and grain weight were most sensitive to HNT during the first 4 d of anthesis, compared with 1–4, 5–8, and 9–12 d before anthesis. At anthesis, reduction in spikelet fertility did not differ significantly between pre‐ and post‐midnight high‐temperature treatments. Our results suggest that greatest sensitivity to HNT during the reproductive stage occurs during the first 4 d of anthesis, providing a reference for future studies involving HNT tolerance in rice.
Yoshiyuki Kinose, Yuji Masutomi, Fumitaka Shiotsu, Keiichi Hayashi, Daikichi Ogawada, Martin Gomez-Garcia, Akiko Matsumura, Kiyoshi Takahashi, Kensuke Fukushi
Journal of Agricultural Meteorology, Volume 76, pp 19-28; doi:10.2480/agrmet.d-19-00045

The publisher has not yet granted permission to display this abstract.
Published: 16 December 2019
by MDPI
Sustainability, Volume 11; doi:10.3390/su11247200

Abstract:
Climatic and non-climatic stressors, such as temperature increases, rainfall fluctuations, population growth and migration, pollution, land-use changes and inadequate gender-specific strategies, are major challenges to coastal agricultural sustainability. In this paper, we discuss all pertinent issues related to the sustainability of coastal agriculture under climate change. It is evident that some climate-change-related impacts (e.g., temperature and rainfall) on agriculture are similarly applicable to both coastal and non-coastal settings, but there are other factors (e.g., inundation, seawater intrusion, soil salinity and tropical cyclones) that particularly impact coastal agricultural sustainability. Coastal agriculture is characterised by low-lying and saline-prone soils where spatial competition with urban growth is an ever-increasing problem. We highlight how coastal agricultural viability could be sustained through blending farmer perceptions, adaptation options, gender-specific participation and integrated coastal resource management into policy ratification. This paper provides important aspects of the coastal agricultural sustainability, and it can be an inspiration for further research and coastal agrarian planning.
Published: 11 December 2019
Atmospheric Chemistry and Physics, Volume 19, pp 14721-14740; doi:10.5194/acp-19-14721-2019

Abstract:
Emissions of methane (CH4) from tropical ecosystems, and how they respond to changes in climate, represent one of the biggest uncertainties associated with the global CH4 budget. Historically, this has been due to the dearth of pan-tropical in situ measurements, which is particularly acute in Africa. By virtue of their superior spatial coverage, satellite observations of atmospheric CH4 columns can help to narrow down some of the uncertainties in the tropical CH4 emission budget. We use proxy column retrievals of atmospheric CH4 (XCH4) from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the nested version of the GEOS-Chem atmospheric chemistry and transport model (0.5∘×0.625∘) to infer emissions from tropical Africa between 2010 and 2016. Proxy retrievals of XCH4 are less sensitive to scattering due to clouds and aerosol than full physics retrievals, but the method assumes that the global distribution of carbon dioxide (CO2) is known. We explore the sensitivity of inferred a posteriori emissions to this source of systematic error by using two different XCH4 data products that are determined using different model CO2 fields. We infer monthly emissions from GOSAT XCH4 data using a hierarchical Bayesian framework, allowing us to report seasonal cycles and trends in annual mean values. We find mean tropical African emissions between 2010 and 2016 range from 76 (74–78) to 80 (78–82) Tg yr−1, depending on the proxy XCH4 data used, with larger differences in Northern Hemisphere Africa than Southern Hemisphere Africa. We find a robust positive linear trend in tropical African CH4 emissions for our 7-year study period, with values of 1.5 (1.1–1.9) Tg yr−1 or 2.1 (1.7–2.5) Tg yr−1, depending on the CO2 data product used in the proxy retrieval. This linear emissions trend accounts for around a third of the global emissions growth rate during this period. A substantial portion of this increase is due to a short-term increase in emissions of 3 Tg yr−1 between 2011 and 2015 from the Sudd in South Sudan. Using satellite land surface temperature anomalies and altimetry data, we find this increase in CH4 emissions is consistent with an increase in wetland extent due to increased inflow from the White Nile, although the data indicate that the Sudd was anomalously dry at the start of our inversion period. We find a strong seasonality in emissions across Northern Hemisphere Africa, with the timing of the seasonal emissions peak coincident with the seasonal peak in ground water storage. In contrast, we find that a posteriori CH4 emissions from the wetland area of the Congo Basin are approximately constant throughout the year, consistent with less temporal variability in wetland extent, and significantly smaller than a priori estimates.
, , Leo Kris Palao, Gudina Feyisa, Arelene Julia Malabayabas, Jorrel K. Aunario, Cornelia Garcia, Jane G. Balanza, Khin Thawda Win, , et al.
Published: 1 December 2019
Field Crops Research, Volume 244; doi:10.1016/j.fcr.2019.107631

The publisher has not yet granted permission to display this abstract.
, Tianmei Wang, Carolin F. Kerl, Britta Planer-Friedrich,
Nature Communications, Volume 10, pp 1-10; doi:10.1038/s41467-019-12946-4

Abstract:
Projections of global rice yields account for climate change. They do not, however, consider the coupled stresses of impending climate change and arsenic in paddy soils. Here, we show in a greenhouse study that future conditions cause a greater proportion of pore-water arsenite, the more toxic form of arsenic, in the rhizosphere of Californian Oryza sativa L. variety M206, grown on Californian paddy soil. As a result, grain yields decrease by 39% compared to yields at today’s arsenic soil concentrations. In addition, future climatic conditions cause a nearly twofold increase of grain inorganic arsenic concentrations. Our findings indicate that climate-induced changes in soil arsenic behaviour and plant response will lead to currently unforeseen losses in rice grain productivity and quality. Pursuing rice varieties and crop management practices that alleviate the coupled stresses of soil arsenic and change in climatic factors are needed to overcome the currently impending food crisis.
Yoshiyuki Kinose, Yuji Masutomi
Adaptation to Climate Change in Agriculture pp 67-80; doi:10.1007/978-981-13-9235-1_5

The publisher has not yet granted permission to display this abstract.
, WonSik Kim
Adaptation to Climate Change in Agriculture pp 97-110; doi:10.1007/978-981-13-9235-1_7

The publisher has not yet granted permission to display this abstract.
Yongzhe Chen, XiaoMing Feng, Bojie Fu, Weiyue Shi, Lichang Yin, Yihe Lv
Water Resources Research, Volume 55, pp 3708-3738; doi:10.1029/2018wr023573

The publisher has not yet granted permission to display this abstract.
, Piera Patrizio, Sylvain Leduc, , Minh Ha-Duong
Published: 1 April 2019
Journal of Cleaner Production, Volume 215, pp 1301-1311; doi:10.1016/j.jclepro.2019.01.065

The publisher has not yet granted permission to display this abstract.
, Robert J Hijmans
Environmental Research Communications, Volume 1; doi:10.1088/2515-7620/ab0856

Abstract:
Climate change can affect crop yield in a given location, and it can also affect where crops are grown. Most assessments of the effect of historical climate change on crop yield has been at the national level, ignoring possibly important subnational variation and climate change adaptation through changes in crop distribution. We analyzed the relationship between growing season temperature, rice yield, and the spatial distribution of rice production in China between 1949 and 2015. Since 1949, rice production in China has moved northwards. Because of this, country level average temperature for rice areas during the growing season was relatively stable, and colder than it would have been without the movement. Temperature has had a very small effect on rice yield at the country level of -0.05 t ha-1 ℃ -1. However, this masks important subnational variation. Increased temperatures were associated with an increase in rice yield (0 – 1.0 t ha-1 ℃-1) in northern provinces and a decrease (-0.6 – 0 t ha-1 ℃-1) in southern provinces of China. While the estimated overall effect of the northward movement on average rice yield in China was only 162 kg ha-1, it does illustrate how crop movements can modify climate change effects and can be an emergent adaptation strategy.
, WonSik Kim, Motoki Nishimori
Journal of Advances in Modeling Earth Systems, Volume 11, pp 99-112; doi:10.1029/2018ms001477

Abstract:
The lack of spatially detailed crop calendars is a significant source of uncertainty in modeling, monitoring, and forecasting crop production. In this paper, we present a rule‐based model to estimate the sowing and harvesting windows of major crops over the global land area. The model considers field workability due to snow cover and heavy rainfall in addition to crop biological requirements for heat, chilling, and moisture. Using daily weather data for the period 1996–2005 as model input, we derive calendars for maize, rice, winter and spring wheat, and soybeans around the year 2000 with a spatial resolution of 0.5° in latitude and longitude. Separate calendars for rainfed and irrigated conditions and three representative varieties (short‐, medium‐ and long‐season varieties) are estimated. The daily probabilities of sowing and harvesting derived using the model well capture the major characteristics of reported calendars. Our modeling reveals that field workability is an important determinant of sowing and harvesting dates and that multicropping patterns influence the calendars of individual crops. The case studies show that the model is capable of capturing multicropping patterns such as triple rice cropping in Bangladesh, double rice cropping in the Philippines, winter wheat‐maize rotations in France, and maize‐winter wheat‐soybean rotations in Brazil. The model outputs are particularly valuable for agricultural and hydrological applications in regions where existing crop calendars are sparse or unreliable.
, Linda See, Juan Carlos Laso Bayas, François Waldner, , Inbal Becker-Reshef, Alyssa Whitcraft, Bettina Baruth, Rogerio Bonifacio, Jim Crutchfield, et al.
Published: 1 January 2019
Agricultural Systems, Volume 168, pp 258-272; doi:10.1016/j.agsy.2018.05.010

Abstract:
Global and regional scale agricultural monitoring systems aim to provide up-to-date information regarding food production to different actors and decision makers in support of global and national food security. To help reduce price volatility of the kind experienced between 2007 and 2011, a global system of agricultural monitoring systems is needed to ensure the coordinated flow of information in a timely manner for early warning purposes. A number of systems now exist that fill this role. This paper provides an overview of the eight main global and regional scale agricultural monitoring systems currently in operation and compares them based on the input data and models used, the outputs produced and other characteristics such as the role of the analyst, their interaction with other systems and the geographical scale at which they operate. Despite improvements in access to high resolution satellite imagery over the last decade and the use of numerous remote-sensing based products by the different systems, there are still fundamental gaps. Based on a questionnaire, discussions with the system experts and the literature, we present the main gaps in the data and in the methods. Finally, we propose some recommendations for addressing these gaps through ongoing improvements in remote sensing, harnessing new and innovative data streams and the continued sharing of more and more data.
Science Advances, Volume 4; doi:10.1126/sciadv.aat4517

Abstract:
Testing our understanding of crop yield responses to weather fluctuations at global scale is notoriously hampered by limited information about underlying management conditions, such as cultivar selection or fertilizer application. Here, we demonstrate that accounting for observed spatial variations in growing seasons increases the variance in reported national maize and wheat yield anomalies that can be explained by process-based model simulations from 34 to 58% and 47 to 54% across the 10 most weather-sensitive main producers, respectively. For maize, the increase in explanatory power is similar to the increase achieved by accounting for water stress, as compared to simulations assuming perfect water supply in both rainfed and irrigated agriculture. Representing water availability constraints in irrigation is of second-order importance. We improve the model’s explanatory power by better representing crops’ exposure to observed weather conditions, without modifying the weather response itself. This growing season adjustment now allows for a close reproduction of heat wave and drought impacts on crop yields.
, Koti Venkata Ramana Murthy
Published: 31 October 2018
Plant Competition in Cropping Systems; doi:10.5772/intechopen.76904

Abstract:
In India, the rice-based cropping system is a major food production system with rice as the first food crop. The cereal-based cropping system is low-yielding and highly nutrient exhaustive resulting in the declining of soil fertility. Summer/pre kharif fallowing leaves on the land fallow for entire season and production of the cropping system is declined. Hence, crops that can improve the fertility status should be included in the cropping system. Development of short duration thermal insensitive rice varieties has encouraged multiple cropping involving a wide range of crops. Diversification of rice-based cropping systems with inclusion of pulses/legumes and oilseeds in summer fallows is one of the options for horizontal expansion, as they are known to improve soil organic matter through biological nitrogen fixation, root exudates, leaf shedding and higher below ground biomass. The strategy for higher yields in the cropping system should be formulated using the combined application of organics, inorganics and biofertilizers coupled with the inclusion of crops in summer fallows for sustainable yields and preservation of soil health.
International Journal of Molecular Sciences, Volume 19; doi:10.3390/ijms19103022

Abstract:
δ-Tocotrienol, an important component of vitamin E, has been reported to possess some physiological functions, such as anticancer and anti-inflammation, however their molecular mechanisms are not clear. In this study, δ-tocotrienol was isolated and purified from rice bran. The anti-inflammatory effect and mechanism of δ-tocotrienol against lipopolysaccharides (LPS) activated pro-inflammatory mediator expressions in RAW264.7 cells were investigated. Results showed that δ-tocotrienol significantly inhibited LPS-stimulated nitric oxide (NO) and proinflammatory cytokine (TNF-α, IFN-γ, IL-1β and IL-6) production and blocked the phosphorylation of c-Jun N-terminal kinase (JNK) and extracellular regulated protein kinases 1/2 (ERK1/2). δ-Tocotrienol repressed the transcriptional activations and translocations of nuclear factor-kappa B (NF-κB) and activator protein-1 (AP-1), which were closely related with downregulated cytokine expressions. Meanwhile, δ-tocotrienol also affected the PPAR signal pathway and exerted an anti-inflammatory effect. Taken together, our data showed that δ-tocotrienol inhibited inflammation via mitogen-activated protein kinase (MAPK) and peroxisome proliferator-activated receptor (PPAR) signalings in LPS-stimulated macrophages.
Prasanna Kumarathilaka, Saman Seneweera, Andrew Meharg,
Published: 1 September 2018
Water Research, Volume 140, pp 403-414; doi:10.1016/j.watres.2018.04.034

The publisher has not yet granted permission to display this abstract.
Akihiko Ito
Climate in Biosphere, Volume 18, pp 53-69; doi:10.2480/cib.j-18-043

Abstract:
Meteorological data are one of the fundamental information not only for studies on agricultural meteorology but also for other research fields and various social and economic activities. In this review, I summarized the current status of broad-scale (i.e. landscape or larger) data of land-surface meteorology applicable to agrometeorological works. The data sets presented here include statistically aggregated or up-scaled data, objective analysis and reanalysis data with meteorological models, teleconnection indices, satellite remote sensing data, and synthetic data for model input. Based on the development of weather monitoring systems and informatics, variety, quality, and coverage of these data sets have greatly improved in last decades. The data development facilitates our researches and operations in agricultural fields with respect to yield estimation, meteorological risk avoidance, optimization of management, and so on. However, we should still pay enough attention to deficiencies (e.g., bias and error, and short coverage) contained in these data, including the characteristics and difference among data sets, and data use policy.
Published: 12 December 2017
by Wiley
Global Change Biology, Volume 24, pp 1029-1045; doi:10.1111/gcb.13967

Abstract:
This study is the first of its kind to quantify possible effects of climate change on rice production in Africa. We simulated impacts on rice in irrigated systems (dry season and wet season) and rainfed systems (upland and lowland). We simulated the use of rice varieties with a higher temperature sum as adaptation option. We simulated rice yields for 4 RCP climate change scenarios and identified causes of yield declines. Without adaptation, shortening of the growing period due to higher temperatures had a negative impact on yields (−24% in RCP 8.5 in 2070 compared with the baseline year 2000). With varieties that have a high temperature sum, the length of the growing period would remain the same as under the baseline conditions. With this adaptation option rainfed rice yields would increase slightly (+8%) but they remain subject to water availability constraints. Irrigated rice yields in East Africa would increase (+25%) due to more favourable temperatures and due to CO2 fertilization. Wet season irrigated rice yields in West Africa were projected to change by −21% or +7% (without/with adaptation). Without adaptation irrigated rice yields in West Africa in the dry season would decrease by −45% with adaptation they would decrease significantly less (−15%). The main cause of this decline was reduced photosynthesis at extremely high temperatures. Simulated heat sterility hardly increased and was not found a major cause for yield decline. The implications for these findings are as follows. For East Africa to benefit from climate change, improved water and nutrient management will be needed to benefit fully from the more favourable temperatures and increased CO2 concentrations. For West Africa, more research is needed on photosynthesis processes at extreme temperatures and on adaptation options such as shifting sowing dates.
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