1,327 research outputs found
Applying agroclimatic seasonal forecasts to improve rainfed maize agronomic management in Colombia
Climate variability affects crop production in multiple and often complex ways. The development and use hybrid crops with greater productivity and tolerance to climate shocks is one of the approaches to climate adaptation and agricultural intensification. Since hybrid crops are more expensive for the producer, risk management is of paramount importance. Here, we pose that there is high potential for the Colombian maize sector to use crop-specific climate services for risk reduction. We used the CERES-Maize crop model connected to seasonal climate forecasts developed via Canonical Correlation Analysis (CCA) across key maize growing areas in Colombia to assess the performance of a maize-specific agroclimatic forecast to inform two key decisions, namely, the choice of sowing dates and genotypes. We find that the agroclimatic models perform well at discriminating yield categories (above, below, and normal) with discrimination capacity of up to 70â80 % for the âbelow normalâ and âabove + below normalâ categories. Consistent with this, agroclimatic forecasts typically predict the optimal planting date with an error of 3 pentads or less. They also predict the optimal choice of genotype correctly around 50â70 % of the time depending on the site or season of interest. Notably, we identify specific cases in which the agroclimatic forecast is misleading but argue that the overall value of the forecasts outweighs these cases. Future work should focus on expanding the scope of the agroclimatic prediction to include other relevant farming decisions that are influenced by climate, and on the improvement of climate forecast performance
Nitrogenutnyttelse i dype jordlag og NâO-dannelse fra klĂžvergras i norsk grovfĂŽrproduksjon
Value creation in Norwegian agriculture is primarily based on milk and meat production, which depend in part on perennial grassland-based forage production. Roughly 60% of the fully cultivated land in Norway is used for grass production
because the climate and growing conditions limit agronomic options for growing food-quality cereals and vegetables in many parts of the country. In addition, Norway has large areas of land which can be exploited as managed pasture or seasonal rangeland.
Globally, there is much focus on improving Nitrogen Use Efficiency (NUE) of crop production to mitigate the perturbation of the global nitrogen (N) cycle accompanying increased food production, with nitrate runoff leading to eutrophication of waterways, and emission of the climate-forcing gas nitrous oxide (N2O). Perennial grasslands located in cold and northern climates are especially vulnerable to large N losses due to poor winter survival, long dormant periods, and
decomposition of frost-killed biomass.
Much of Norwayâs cultivable land lies in the hemiboreal climate zone, to which many perennial grassland species are adapted. Perennial ryegrass (Lolium perenne L.), which is grown for its good yield potential but is less winter-hardy, can increase the risk of N losses if it survives poorly. Clovers, which are N-rich and more frostsensitive than grasses, also contribute to N losses, especially to winter-associated N2O production. On the other hand, clovers can increase the NUE of forage swards by partially replacing the need for fertilizer, and via diversity effects with grasses which increase sward yields and protein concentration.
Opportunities for improving NUE lie belowground. The dense mat of roots in the topsoil of grasslands cycles and stores massive amounts of N (and carbon and other nutrients) and is the locus for microbiological N transformations which also form N2O. Some grassland species are capable of sending roots far below the densest root zone and recapturing N which has leached downwards. Diversity effects, not only between clovers and grasses, but also between grass species, greatly influence how a grassland sward utilizes the N throughout the soil profile and throughout the growing season.
Using a stable isotope method with a novel slow-release 15NH4+ label, we studied deep root N uptake in well-established perennial forage swards. We studied five grass and two clover species in pure stands, over two growing seasons and a variety of weather events (Paper I), as well as the diversity effects, or results arising from species interactions, on yields and deep N utilization by two grass-clover mixtures in the second growing season (Paper II).
Tall-growing grass species proved effective at acquiring N from below the densest root zone in the mid- to late growing season, after apparently needing time to reestablish deep root activity in spring (Paper I). The affinity for NH4+, the winter
hardiness, and the growth vigor of these species proved to be more important functional traits for deep N acquisition than purported root depth (Paper I). When in mixture, the importance of growth vigor âcompetitivenessâ became even more
important, stimulating changes in deep N uptake behavior between species (Paper II). Clovers contributed to higher forage yields wherein grasses had higher N content, and mixtures utilized deep N as well as grass pure stands, thus diversity
effects led to the best combination of yields and NUE (Paper II).
In an adjacent field, we monitored N2O formation in grass, clover, and grass-clover swards throughout winter, including prolonged reducing soil conditions under snowpack, and during spring thaw (Paper III). We explored how liming, hypothesized to reduce N2O formation by denitrification, affected N2O emissions under these conditions in situ. Use of a fast-chamber robot allowed us to measure N2O fluxes during thaw events at a high frequency, while we used pre-installed soil air probes and gas chromatography to monitor gas levels in subnivean soil air as indicators for microbiological N-cycling.
Off-season N2O emissions were lowest in grasses, highest in red clover, and moderate in grass-clover mixtures, which emitted less than expected (Paper III). Although liming reduced subsoil N2O accumulation under snowpack in grass-only
swards, we think that in clover-containing swards higher pH stimulated nitrification of N released by frost-killed clover biomass to NO3-, in turn stimulating N2O production by nitrification or by providing substrate for denitrification. The apparent diversity effect wherein grass-clover mixtures emitted less N2O than expected was observed in both limed and non-limed plots in autumn. However, this effect was weaker in limed mixtures in the spring, suggesting increased N cycling in the higher-pH soils became more important than decomposition of clover biomass to N2O production as the next growing season began.
This thesis demonstrates synergistic diversity effects of combining clover with grasses, which results in reduced N losses combined with increased protein yields, and possibly reducing the severity of N2O formation due to clovers over winter. NUE and N2O emission in Norwegian forage production can be managed by careful choice of forage species, particularly considering the proportions of clover and appropriate pH management.Verdiskaping i det Norske landbruket baserer seg hovedsakelig pÄ produksjon av melk og kjÞtt, og er delvis avhengig av fÎrproduksjon basert pÄ flerÄrig eng. Cirka 60% av fulldyrket areal i Norge brukes til flerÄrig grasvekst som er velegnet ogsÄ i deler av landet hvor klima og vekstforhold begrenser korn- og grÞnnsaksproduksjon. I tillegg har Norge store omrÄder med areal som kan utnyttes som gjÞdslet innmarksbeite eller utmarksbeite.
Globalt er det stor fokus pÄ Ä forbedre nitrogeneffektiviteten (nitrogen use efficiency; NUE) i planteproduksjon for Ä motvirke forstyrrelsen av den globale nitrogensyklusen som fÞlger med Þkt matproduksjon. Nitratavrenning fÞrer til eutrofiering av vann og vassdrag, og N bruk i matproduksjon Þker utslipp av klimagassen lystgass (N2O). FlerÄrig eng som finnes i kalde og nordlige omrÄder er spesielt sÄrbare for nitrogen-tap pÄ grunn av dÄrlig overvintring, lange perioder uten vekst, og nedbrytning av frostdrept biomasse.
Mye av Norges fulldyrkede areal ligger i klimasonen «hemiboreal», hvor mange flerÄrige grasarter er tilpasset et kaldt vinterklima gjennom vinterherding. FlerÄrig raigras (Lolium perenne L.), som dyrkes pÄ grunn av sitt gode avlingspotensial men som er mindre vinterhardt, kan Þke sjansen for N tap hvis det overlever dÄrlig. KlÞver, som er N-rik og som tÄler frost dÄrligere enn gras, bidrar sÊrlig til N-tap, spesielt om vinteren i form av lystgass, men pÄ den andres siden kan klÞver Þke NUE i fÎrproduksjonen ved Ä delvis erstatte tilfÞrt gjÞdselmengden. Sammen med gras bidrar klÞver til diversitetseffekter som Þker fÎravlingene og proteinmengde.
Muligheter for Ä Þke NUE ligger under bakken. Den tette matten av rÞtter til grasvekster i Þvre matjordlaget lagrer og sirkulerer store mengder N (samt karbon og andre nÊringsstoffer). I dette omrÄdet omsetter mikroorganismer C og N og danner N2O underveis. Noen arter kan ha rÞttene langt ned i jorden og fanger opp N som har blitt vasket ned i jordprofilen. Diversitetseffekter, ikke bare mellom klÞver og gras, men ogsÄ mellom ulike grasarter, pÄvirker i stor grad N-utnyttelsen i hele jordprofilen, og gjennom sesongen.
Vi kombinerte en stabil isotopmetode med en unik langvarig 15NH4+ merking for Ä undersÞke N-opptak av dypgÄende rÞtter i veletablerte flerÄrige eng. Vi undersÞkte fem gras- og to klÞverarter i monokultur, i to vekstsesonger og gjennom flere ulike vÊrhendelser (Artikkel I). Vi undersÞkte diversitetseffekter, eller resultater som oppstÄr fra interaksjoner mellom plantearter, pÄ avlinger og dyp N-utnyttelse i to blandinger av gras og klÞver (klÞvergras) i den andre vekstsesongen (Artikkel II).
De hÞyvoksende grasartene utnyttet N fra dyp jord effektivt fra midten til slutten av vekstsesongen, men trengte tid til Ä gjenopprette dyp rot aktivitet om vÄren (Artikkel I). Affinitet for NH4+ opptak, grad av vinterherding og vekststyrke av disse artene viste seg Ä vÊre viktigere funksjonelle egenskaper for dyp N-utnyttelse enn tidligere antatt rotdybde (Artikkel I). I klÞvergras blandinger ble vekststyrke eller konkurranseevne enda viktigere, og stimulerte artene til Ä endre dyp N-utnyttelse pÄ ulike mÄter (Artikkel II). KlÞveren bidro til Þkt fÎravling og hÞyere N-innhold i gras. KlÞvergras blandinger utnyttet ogsÄ dyp N like godt som grasmonokultur. Derfor fÞrte diversitetseffekter til den beste kombinasjonen av avling, kvalitet og NUE (Artikkel II).
I et tilgrensede felt undersÞkte vi N2O produksjon i gras-, klÞver-, og klÞvergraseng gjennom en vinter, som inkluderte en periode med langvarig reduserende jordforhold under snÞdekke, og i lÞpet av tiningsperioden om vÄren (Artikkel III). Vi undersÞkte hvordan kalking, som er antatt Ä redusere N2O-dannelse fra denitrifikasjon, pÄvirket N2O-utslipp under slike forhold in situ. Vi mÄlte N2O-fluks med hÞy frekvens under fryse-tinehendelser ved bruk av en robot utstyrt med hurtigkamre («fast-box» chambers). Vi brukte forhÄndsinstallerte jordluftsonder og gasskromatografi for Ä undersÞke gassnivÄer i jordluftet gjennom vinteren, som brukes som indikatorer for mikrobiologisk N-prosesser.
Utslipp av N2O om vinteren var minst i gras, stÞrst i rÞdklÞver, og moderat i klÞvergras, hvor utslippet var lavere enn forventet (Artikkel III). Mens kalking reduserte N2O i jordluften under snÞdekke i rene grasruter, resultatene indikerer at den hÞyere pH kan ha stimulert nitrifikasjon etter nedbrytning av N-rik biomasse fra frostskadet klÞver, og at dette fÞrte til Þkt N2O-produksjon ved nitrifikasjon eller ved Ä gi substrat for Þkt denitrifikasjon. Den tydelige diversitetseffekter der klÞvergras ga mindre N2O utslipp enn forventet ble observert bÄde i kalket og ikkekalket vekster om hÞsten, men effekten var svakere i kalket jord om vÄren. Det kan betyr at Þkt N-sirkulering ved hÞyere pH ble en viktigere kilde for N2O enn
nedbrytningen av klĂžverbiomasse ved start av vekstsesongen.
Denne avhandlingen dokumenterer noen synergiske diversitetseffekter av Ä blande klÞver med gras i grovfÎrproduksjon, som resulterer i redusert N-tap kombinert med Þkt proteinavling, og muligens med en redusert grad av N2O-utslipp utenom vekstsesongen. NUE og N2O-utslipp i norske fÎrproduksjon kan pÄvirkes ved nÞye sammensetning av fÎrarter, spesielt med tanke pÄ andel klÞver i vekst, og
hensiktsmessig pH-behandling
Optimizing the carbonic anhydrase temperature response and stomatal conductance of carbonyl sulfide leaf uptake in the Simple Biosphere model (SiB4)
Carbonyl sulfide (COS) is a useful tracer to estimate
gross primary production (GPP) because it shares part of the uptake pathway
with CO2. COS is taken up in plants through hydrolysis, catalyzed by
the enzyme carbonic anhydrase (CA), but is not released. The Simple
Biosphere model version 4 (SiB4) simulates COS leaf uptake using a
conductance approach. SiB4 applies the temperature response of the RuBisCo
enzyme (used for photosynthesis) to simulate the COS leaf uptake, but the CA enzyme might respond differently to temperature. We introduce a new
temperature response function for CA in SiB4, based on enzyme kinetics with
an optimum temperature. Moreover, we determine BallâWoodrowâBerry (BWB)
model parameters for stomatal conductance (gs) using observation-based estimates of COS flux, GPP, and gs along with meteorological measurements in an evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF). We find that CA has optimum temperatures of 20ââC (ENF) and 36ââC (DBF), which is lower than that of RuBisCo (45ââC), suggesting that canopy temperature changes can critically affect CA's catalyzation activity. Optimized values for the BWB offset parameter are similar to the original value (0.010â±â0.003âmolâmâ2âsâ1), and optimized values for the BWB slope parameter (ENF: 16.4, DBF: 11.4) are higher than the original value (9.0) at both sites. The optimization reduces prior errors on all parameters by more than 50â% at both stations. We apply the optimized gi and gs parameters in
SiB4 site simulations, thereby improving the timing and peak of COS
assimilation. In addition, we show that SiB4 underestimates the leaf
humidity stress under conditions where high vapor pressure deficit (VPD) should limit gs in the afternoon, thereby overestimating gs. Furthermore, global COS biosphere sinks with optimized parameters show smaller COS uptake in regions where the air temperature is over 25ââC, mostly in the tropics, and larger uptake in regions where the temperature is below 25ââC. This change
corresponds with reported deficiencies in the global COS fluxes, such as
missing sinks at high latitudes and required sources in the tropics. Using
our optimization and additional observations of COS uptake over various
climate and plant types, we expect further improvements in global COS
biosphere flux estimates.</p
Statistical and machine learning modelling of UK surface ozone
In addition to atmospheric observations, numerical models are crucial to understand the impacts of human activities on the environment, from attributing poor air quality to assessing climate change impacts. While process-based models, such as chemistry transport models (CTMs), are widely used, recent data science advances enable greater use of statistical and machine learning methods as alternatives to describe and predict atmospheric composition. State-of-the-art data science methods can be faster to run than CTMs and used at high temporal and spatial resolutions due to codebase efficiencies. This thesis focuses on modelling UK surface ozone and its drivers (high levels of which are detrimental to human and plant health) through the development and novel application of sophisticated statistical and machine learning techniques. Motivated by possible adverse effect of climate change on ozone concentrations, a temperature-dependent Extreme Value Analysis is used to explore the probability, magnitude, and frequency of extreme ozone events over recent decades. For 2010â2019, it is found that the 1-year return level of daily maximum 8-h mean (MDA8) ozone exceeds the âmoderateâ health threshold (100 ”g/m3) at >90% of sites, but that the probability of extreme ozone events has markedly decreased since the 1980s. A machine learning methodology to downscale and bias correct a CTM (EMEP4UK) ozone surface was developed and evaluated. Compared to the unadjusted CTM, the downscaled surface exhibits a lower bias in reproducing MDA8 ozone allowing more robust assessments of important policy metrics. Analysis of the downscaled product (2014â2018) reveals on average 27% of the UK fails the government long-term objective for MDA8 ozone to not exceed 100 ”g/m3 more than 10 times per year, compared to 99% in the unadjusted CTM. A classification-based machine learning analysis into high-level ozone drivers was also performed and shows a robust relationship between ozone and temperature. The method is demonstrated to offer remarkable promise as a tool with which to forecast the presence of high-level ozone. Despite a UK focus, the data-driven methods developed and applied here are applicable to modelling ozone in other regions of the world where measurements exist
Improved Estimation of the Gross Primary Production of Europe by Considering the Spatial and Temporal Changes in Photosynthetic Capacity from 2001 to 2016
The value of leaf photosynthetic capacity (Vcmax) varies with time and space, but state-of-the-art terrestrial biosphere models rarely include such Vcmax variability, hindering the accuracy of carbon cycle estimations on a large scale. In particular, while the European terrestrial ecosystem is particularly sensitive to climate change, current estimates of gross primary production (GPP) in Europe are subject to significant uncertainties (2.5 to 8.7 Pg C yrâ1). This study applied a process-based Farquhar GPP model (FGM) to improve GPP estimation by introducing a spatially and temporally explicit Vcmax derived from the satellite-based leaf chlorophyll content (LCC) on two scales: across multiple eddy covariance tower sites and on the regional scale. Across the 19 EuroFLUX sites selected for independent model validation based on 9 plant functional types (PFTs), relative to the biome-specific Vcmax, the inclusion of the LCC-derived Vcmax improved the model estimates of GPP, with the coefficient of determination (R2) increased by 23% and the root mean square error (RMSE) decreased by 25%. Vcmax values are typically parameterized with PFT-specific Vcmax calibrated from flux tower observations or empirical Vcmax based on the TRY database (which includes 723 data points derived from Vcmax field measurements). On the regional scale, compared with GPP, using the LCC-derived Vcmax, the conventional method of fixing Vcmax using the calibrated Vcmax or TRY-based Vcmax overestimated the annual GPP of Europe by 0.5 to 2.9 Pg C yrâ1 or 5 to 31% and overestimated the interannually increasing GPP trend by 0.007 to 0.01 Pg C yrâ2 or 14 to 20%, respectively. The spatial pattern and interannual change trend of the European GPP estimated by the improved FGM showed general consistency with the existing studies, while our estimates indicated that the European terrestrial ecosystem (including part of Russia) had higher carbon assimilation potential (9.4 Pg C yrâ1). Our study highlighted the urgent need to develop spatially and temporally consistent Vcmax products with a high accuracy so as to reduce uncertainties in global carbon modeling and improve our understanding of how terrestrial ecosystems respond to climate change
Use of remote sensingâderived fPAR data in a grapevine simulation model for estimating vine biomass accumulation and yield variability at subâfield level
Grapevine simulation models are mostly used to estimate plant development, growth and yield at plot scale. However, the spatial variability of pedologic and micro-climatic conditions can influence vine growth, leading to a sub-field heterogeneity in plant vigor and final yield that may be better estimated through the assimilation of high spatial resolution data in crop models. In this study, the spatial variability of grapevine intercepted radiation at fruit-set was used as input for a grapevine simulation model to estimate the variability in biomass accumulation and yield in two Tuscan vineyards (Sites A and B). In Site A, the model, forced with intercepted radiation data as derived from the leaf area index (LAI), measured at canopy level in three main vigor areas of the vineyard, provided a satisfactory simulation of the final pruning weight (r2â=â0.61; RMSEâ=â19.86 dry matter g mâ2). In Site B, Normalized Difference Vegetation Index (NDVI) from Sentinel-2A images was firstly re-scaled to account for canopy fraction cover over the study areas and then used as a proxy for grapevine intercepted radiation for each single pixel. These data were used to drive the grapevine simulation model accounting for spatial variability of plant vigor to reproduce yield variability at pixel scale (r2â=â0.47; RMSEâ=â75.52 dry matter g mâ2). This study represents the first step towards the realization of a decision tool supporting winegrowers in the selection of the most appropriate agronomic practices for reducing the vine vigor and yield variability at sub-field level
Exposure to climate and vulnerability to food insecurity in Ethiopia
Food insecurity is increasing in severity, with more than two billion people experiencing a lack of nutritious and affordable food. Research in climate change and food systems has generally emphasized crop production and child anthropometric outcomes, with limited focus on the complex linkages between climate variability, household food security, and gender. This paper examines the impacts of climate on diet diversity and coping mechanisms by drawing on nationally representative longitudinal data from Ethiopia through the Living Standards Measurement Surveys â Intensive Surveys on Agriculture (LSMS-ISA) and multiple measures of food insecurity to address vulnerability and resilience. To measure climate exposures, high- resolution data on rainfall and heat shocks from UCSBâs CHIRPS and CHIRTSmax will be used at a 5km climate grid (0.05o). A regression of food security outcomes as a function of climate anomalies, gender, controls, and interactions will be used to directly measure inequities across households and vulnerability to food insecurity.Master of Art
Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach
Long-range weather forecasts provide predictions of atmospheric, ocean and land surface conditions that can potentially be used in land surface and hydrological models to predict the water and energy status of the land surface or in crop growth models to predict yield for water resources or agricultural planning. However, the coarse spatial and temporal resolutions of available forecast products have hindered their widespread use in such modelling applications, which usually require high-resolution input data. In this study, we applied sub-seasonal (up to 4 months) and seasonal (7 months) weather forecasts from the latest European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal forecasting system (SEAS5) in a land surface modelling approach using the Community Land Model version 5.0 (CLM5). Simulations were conducted for 2017â2020 forced with sub-seasonal and seasonal weather forecasts over two different domains with contrasting climate and cropping conditions: the German state of North Rhine-Westphalia (DE-NRW) and the Australian state of Victoria (AUS-VIC). We found that, after pre-processing of the forecast products (i.e. temporal downscaling of precipitation and incoming short-wave radiation), the simulations forced with seasonal and sub-seasonal forecasts were able to provide a model output that was very close to the reference simulation results forced by reanalysis data (the mean annual crop yield showed maximum differences of 0.28 and 0.36âtâhaâ1 for AUS-VIC and DE-NRW respectively). Differences between seasonal and sub-seasonal experiments were insignificant. The forecast experiments were able to satisfactorily capture recorded inter-annual variations of crop yield. In addition, they also reproduced the generally higher inter-annual differences in crop yield across the AUS-VIC domain (approximately 50â% inter-annual differences in recorded yields and up to 17â% inter-annual differences in simulated yields) compared to the DE-NRW domain (approximately 15â% inter-annual differences in recorded yields and up to 5â% in simulated yields). The high- and low-yield seasons (2020 and 2018) among the 4 simulated years were clearly reproduced in the forecast simulation results. Furthermore, sub-seasonal and seasonal simulations reflected the early harvest in the drought year of 2018 in the DE-NRW domain. However, simulated inter-annual yield variability was lower in all simulations compared to the official statistics. While general soil moisture trends, such as the European drought in 2018, were captured by the seasonal experiments, we found systematic overestimations and underestimations in both the forecast and reference simulations compared to the Soil Moisture Active Passive Level-3 soil moisture product (SMAP L3) and the Soil Moisture Climate Change Initiative Combined dataset from the European Space Agency (ESA CCI). These observed biases of soil moisture and the low inter-annual differences in simulated crop yield indicate the need to improve the representation of these variables in CLM5 to increase the model sensitivity to drought stress and other crop stressors.</p
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