scholarly journals Lower air pollution during COVID-19 lock-down: improving models and methods estimating ozone impacts on crops

2020 ◽  
Vol 378 (2183) ◽  
pp. 20200188 ◽  
Author(s):  
Frank Dentener ◽  
Lisa Emberson ◽  
Stefano Galmarini ◽  
Giovanni Cappelli ◽  
Anisoara Irimescu ◽  
...  
Keyword(s):  
Crop Yields ◽  
Growing Season ◽  
Road Transport ◽  
Initial Assessment ◽  
Emission Reductions ◽  
International Shipping ◽  
Crop Models ◽  
Global Emission ◽  
Emission Changes ◽  
Ozone Precursor

We suggest that the unprecedented and unintended decrease of emissions of air pollutants during the COVID-19 lock-down in 2020 could lead to declining seasonal ozone concentrations and positive impacts on crop yields. An initial assessment of the potential effects of COVID-19 emission reductions was made using a set of six scenarios that variously assumed annual European and global emission reductions of 30% and 50% for the energy, industry, road transport and international shipping sectors, and 80% for the aviation sector. The greatest ozone reductions during the growing season reached up to 12  ppb over crop growing regions in Asia and up to 6 ppb in North America and Europe for the 50% global reduction scenario. In Europe, ozone responses are more sensitive to emission declines in other continents, international shipping and aviation than to emissions changes within Europe. We demonstrate that for wheat the overall magnitude of ozone precursor emission changes could lead to yield improvements between 2% and 8%. The expected magnitude of ozone precursor emission reductions during the Northern Hemisphere growing season in 2020 presents an opportunity to test and improve crop models and experimentally based exposure response relationships of ozone impacts on crops, under real-world conditions. This article is part of a discussion meeting issue ‘Air quality, past present and future’.

scholarly journals Declining risk of ozone impacts on vegetation in Europe 1990–2050 due to reduced precursor emissions in a changed climate

2014 ◽  
Vol 11 (1) ◽  
pp. 625-655
Author(s):  
J. Klingberg ◽  
M. Engardt ◽  
P. E. Karlsson ◽  
J. Langner ◽  
H. Pleijel
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Emission Scenario ◽  
Growing Season ◽  
Future Climate ◽  
Ozone Exposure ◽  
Emission Reductions ◽  
Ozone Uptake ◽  
Ozone Concentrations ◽  
Ozone Precursor

Abstract. The impacts of climate change and changes in ozone precursor emission on ozone exposure (AOT40) of the vegetation in Europe were investigated. In addition, meteorological conditions influencing stomatal uptake of ozone were analysed to find out if climate change is likely to affect the risk for ozone damage to vegetation. Climate simulations based on the IPCC SRES A1B scenario were combined with ozone precursor emission changes from the RCP4.5 scenario and used as input to the Eulerian Chemical Transport Model MATCH from which projections of ozone concentrations were derived. Provided that the climate projections are realistic and the emission reductions of the emission scenario are undertaken, the ozone exposure of vegetation over Europe will be significantly reduced between the two time periods 1990–2009 and 2040–2059. This decline in AOT40 is larger than the reduction in average ozone concentrations. The reduction is driven by the emission reductions assumed by the RCP4.5 emission scenario, rather than changes in the climate. Higher temperatures in a future climate will result in a prolonged growing season over Europe as well as larger temperature sums during the growing season. Both the extended growing season and higher temperatures may enhance ozone uptake by plants in colder parts of Europe. The future climate suggested by the regional climate model will be dryer in terms of higher vapour pressure deficit (VPD) and lower soil moisture in southern Europe, which may reduce ozone uptake. VPD and soil moisture was not projected to change in north and north-west Europe to an extent that would influence ozone uptake by vegetation. This study shows that substantial reductions of ozone precursor emissions have the potential to strongly reduce the risk for ozone effects on vegetation, even if concurrent climate change promotes ozone formation.

Applicability of crop models in the context of parametric insurance – a Caribbean case study

2021 ◽  
Author(s):  
Beatrice Monteleone ◽  
Luigi Cesarini ◽  
Rui Figueiredo ◽  
Mario Martina
Keyword(s):  
Real Time ◽  
Crop Yield ◽  
Vegetation Index ◽  
Crop Yields ◽  
Growing Season ◽  
Gridded Data ◽  
Crop Models ◽  
Weather Events ◽  
Weather Stations ◽  
Weather Parameters

<p>Evaluating the impacts of weather events on the agricultural sector is of high importance. Weather has a huge influence on crop performance and agricultural system management, particularly in those countries where agriculture is mainly rainfed. Climate change is expected to further affect farmers’ incomes since the risk of extreme weather events with a relevant impact on crop yields is predicted to increase.</p><p>Appropriate strategies to deal with the economic impacts of agriculture need to be developed, to enable farmers to quickly recover after a disaster. In this context, weather-based index insurance (also known as parametric insurance) plays a key role since it allows farmers to receive financial aid soon after a disaster occurs.</p><p>This study evaluates the applicability of crop models run with gridded data in the framework of index-based insurance to assess their added value in providing estimations of crop yield in case of drought events.</p><p>At first, the cropland area is identified using satellite data on Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) retrieved from various sources, such as Sentinel and Landsat. Crop Type maps are then produced to identify the location of the different crops grown in a region. Then, weather data coming from stations are exploited to run the AquaCrop crop model and estimate the crop yield for the areas near the weather stations.</p><p>Since in many countries weather stations are often missing or do not record continuously, the AquaCrop model is also run with gridded data coming from reanalysis, specifically ERA, which is a product released by the European Centre for Medium Range Weather Forecast and has the advantage to provide daily estimation of  multiple weather parameters on a 0.25° grid. In addition, ERA5 has a short latency time (in the order of days) and thus allows a near-real time monitoring of the crop growing season. The AquaCrop outputs obtained when the model is run with the station data are then compared to the ones obtained when the model is run with gridded data. The performance of the two model configurations (weather parameters coming from stations or from ERA5) in estimating yield reductions during drought events, previously identified using the Probabilistic Precipitation Vegetation Index (PPVI), are evaluated.</p><p>The framework was applied in the context of the Dominican Republic, a Caribbean country in which 52% of the national territory is devoted to agriculture. The Dominican agricultural industry has as main products cocoa, tobacco, sugarcane, coffee, cotton, rice, beans, potatoes, corn and bananas. Results shows that gridded data can be a valuable tool to provide near-real time estimates of the crop growing season and thus help in forecasting final crop yields in near-real time.</p>

scholarly journals Combined simulation and optimization framework for irrigation scheduling in agriculture fields

2021 ◽  
Author(s):  
Mireia Fontanet ◽  
Daniel Fernàndez-Garcia ◽  
Gema Rodrigo ◽  
Francesc Ferrer ◽  
Josep Maria Villar
Keyword(s):  
Crop Yields ◽  
Root Zone ◽  
Growing Season ◽  
Irrigation Scheduling ◽  
Irrigated Agriculture ◽  
Traditional Methods ◽  
Simulation And Optimization ◽  
Optimization Framework ◽  
Net Margin ◽  
Combined Simulation

AbstractIn the context of growing evidence of climate change and the fact that agriculture uses about 70% of all the water available for irrigation in semi-arid areas, there is an increasing probability of water scarcity scenarios. Water irrigation optimization is, therefore, one of the main goals of researchers and stakeholders involved in irrigated agriculture. Irrigation scheduling is often conducted based on simple water requirement calculations without accounting for the strong link between water movement in the root zone, soil–water–crop productivity and irrigation expenses. In this work, we present a combined simulation and optimization framework aimed at estimating irrigation parameters that maximize the crop net margin. The simulation component couples the movement of water in a variably saturated porous media driven by irrigation with crop water uptake and crop yields. The optimization component assures maximum gain with minimum cost of crop production during a growing season. An application of the method demonstrates that an optimal solution exists and substantially differs from traditional methods. In contrast to traditional methods, results show that the optimal irrigation scheduling solution prevents water logging and provides a more constant value of water content during the entire growing season within the root zone. As a result, in this case, the crop net margin cost exhibits a substantial increase with respect to the traditional method. The optimal irrigation scheduling solution is also shown to strongly depend on the particular soil hydraulic properties of the given field site.

scholarly journals Modeling the Impact of Climate Changes on Crop Yield: Irrigated vs. Non-Irrigated Zones in Mississippi

2021 ◽  
Vol 13 (12) ◽  
pp. 2249
Author(s):  
Sadia Alam Shammi ◽  
Qingmin Meng
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Crop Yields ◽  
Negative Impact ◽  
Positive Impact ◽  
Information Criterion ◽  
Growing Season ◽  
Maximum Temperature ◽  
Linear Regression Models ◽  
The Impact

Climate change and its impact on agriculture are challenging issues regarding food production and food security. Many researchers have been trying to show the direct and indirect impacts of climate change on agriculture using different methods. In this study, we used linear regression models to assess the impact of climate on crop yield spatially and temporally by managing irrigated and non-irrigated crop fields. The climate data used in this study are Tmax (maximum temperature), Tmean (mean temperature), Tmin (minimum temperature), precipitation, and soybean annual yields, at county scale for Mississippi, USA, from 1980 to 2019. We fit a series of linear models that were evaluated based on statistical measurements of adjusted R-square, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). According to the statistical model evaluation, the 1980–1992 model Y[Tmax,Tmin,Precipitation]92i (BIC = 120.2) for irrigated zones and the 1993–2002 model Y[Tmax,Tmean,Precipitation]02ni (BIC = 1128.9) for non-irrigated zones showed the best fit for the 10-year period of climatic impacts on crop yields. These models showed about 2 to 7% significant negative impact of Tmax increase on the crop yield for irrigated and non-irrigated regions. Besides, the models for different agricultural districts also explained the changes of Tmax, Tmean, Tmin, and precipitation in the irrigated (adjusted R-square: 13–28%) and non-irrigated zones (adjusted R-square: 8–73%). About 2–10% negative impact of Tmax was estimated across different agricultural districts, whereas about −2 to +17% impacts of precipitation were observed for different districts. The modeling of 40-year periods of the whole state of Mississippi estimated a negative impact of Tmax (about 2.7 to 8.34%) but a positive impact of Tmean (+8.9%) on crop yield during the crop growing season, for both irrigated and non-irrigated regions. Overall, we assessed that crop yields were negatively affected (about 2–8%) by the increase of Tmax during the growing season, for both irrigated and non-irrigated zones. Both positive and negative impacts on crop yields were observed for the increases of Tmean, Tmin, and precipitation, respectively, for irrigated and non-irrigated zones. This study showed the pattern and extent of Tmax, Tmean, Tmin, and precipitation and their impacts on soybean yield at local and regional scales. The methods and the models proposed in this study could be helpful to quantify the climate change impacts on crop yields by considering irrigation conditions for different regions and periods.

SOIL CONDITIONERS—THEIR EFFECT ON COARSE-TEXTURED SOILS, CROP YIELDS AND COMPOSITION

1957 ◽  
Vol 37 (2) ◽  
pp. 102-112
Author(s):  
R. M. Holmes ◽  
S. J. Toth
Keyword(s):  
Structural Changes ◽  
Crop Yields ◽  
Cultural Practices ◽  
Sandy Soils ◽  
Growing Season ◽  
Soil Aggregate ◽  
Crop Response ◽  
The Third ◽  
Soil Conditioners ◽  
The Stability

Crop response to soil structural changes caused by soil conditioner amendments was studied in several different sandy soils of New Jersey. The response varied with the crop and treatment. Those chemicals that were slightly hydrophobic were most effective and generally crop response was greatest on these treatments. Cations such as Na may be added in large amounts as part of some conditioners, and this may result in reduced uptake of other nutrients such as Mg. and K. Except for this effect, conditioners did not reduce nutrient uptake by plants. When elements such as Na and N are added in large amounts as part of some conditioners, there may be an increased uptake of these nutrients.Catalin and VAMA conditioners produced a dry surface mulch which appeared to reduce evaporation. Moisture reserves were, therefore, preserved through a drought and this resulted in increased growth of crops over those grown on other treatments. Cultural practices destroyed the stability of the conditioned aggregates, since in most cases the effect had largely disappeared by the third growing season. Chemicals which were effective in soil aggregate stabilization were also effective as anti-crustants when crusting was a problem.

scholarly journals PRODUCTIVITY OF PERENNIAL GRASSES AND MIXTURES WITH DIFFERENT SOWING DATES IN WESTERN SIBERIA

2020 ◽  
pp. 24-32
Author(s):  
V. A. Petruk
Keyword(s):  
Dry Matter ◽  
Crop Yields ◽  
Growing Season ◽  
Single Species ◽  
Field Studies ◽  
Perennial Grasses ◽  
First Year ◽  
Sowing Dates ◽  
Second Year ◽  
Sowing Period

The results of field studies for 2017 - 2019 are presented. yields of perennial grasses sown at different times of the growing season. Spring, summer, and winter sowing periods were compared. Alfalfa, clover, rump, and also their mixtures were sown in 2017 under the cover of barley. The value of the cover crop yield of spring and summer sowing periods did not differ significantly and amounted to 4-5 t / ha of absolutely dry matter. Winter barley crops have not formed. On average, over 2 years of use, the highest yields were observed in alfalfa-crust grass mixtures - 3.4 t / ha of absolutely dry matter. The lowest yield was obtained in the single-species seeding of the rump. Correspondingly, in the spring, summer and winter periods of sowing, the yield of rump was 1.6; 1.1 and 1.3 t / ha. With a late sowing period, the yield of perennial grasses is significantly lower compared to spring and summer. With winter sowing periods, the yield was the highest for grass stands of alfalfa and alfalfacrust grass mixture - 2.3 and 2.4 t / ha. It should be noted that in the second year of use, the yield by the sowing dates in single-species crops and grass mixtures is leveled. The winter crops of perennial grasses in the first year of use formed a low yield. Only in the second year (third year of life) the productivity of perennial grasses of winter sowing began to increase. Consequently, in the area under perennial grasses of the winter sowing period, during one growing season (the next year after sowing), the crop was not actually formed. Based on the data obtained, production can be recommended for spring and summer planting of perennial grasses under the cover of barley. The winter sowing period provides economically valuable crop yields only by the third year of life.

Estimating the water use efficiency of spring barley using crop models

2018 ◽  
Vol 156 (5) ◽  
pp. 628-644 ◽  
Author(s):  
E. Pohanková ◽  
P. Hlavinka ◽  
M. Orság ◽  
J. Takáč ◽  
K. C. Kersebaum ◽  
...  
Keyword(s):  
Water Use Efficiency ◽  
Water Use ◽  
Spring Barley ◽  
Growing Season ◽  
Above Ground Biomass ◽  
Grain Yields ◽  
Crop Models ◽  
Average Value ◽  
Ground Biomass ◽  
Use Efficiency

AbstractIn the current study, simulations by five crop models (WOFOST, CERES-Barley, HERMES, DAISY and AQUACROP) were compared for 7–12 growing seasons of spring barley (Hordeum vulgare) at three sites in the Czech Republic. The aims were to compare how various process-based crop models with different calculation approaches simulate different values of transpiration (Ta) and evapotranspiration (ET) based on the same input data and compare the outputs of these simulations with reference data. From the outputs of each model, the water use efficiency (WUE) from Ta (WUETa) and from actual ET (WUEETa) was calculated for grain yields and above-ground biomass yield. The results of the first part of the study show that the model with the Penman approach for calculating ET simulates lower actual ET (ETa) sums, at an average of 250 mm during the growing season, than other models, which use the Penman–Monteith approach and simulate 330 mm on average during the growing season. In the second part of the current study, WUE reference values in the range 1.9–2.4 kg/m3were calculated for spring barley and grain yield. Values of WUETa/WUEETacalculated from the outputs of individual models for grain yields and above-ground biomass yields ranged from 2.0/1.0 to 5.9/3.8 kg/m3with an average value of 3.2/2.0 kg/m3and from 3.9/2.1 to 10.5/6.8 kg/m3with an average value of 6.5/4.0 kg/m3, respectively. The results confirm that the average values of all models are nearest to actual values.

scholarly journals Evaluation of Different Crop Models for Simulating Rice Development and Yield in the U.S. Mississippi Delta

Agronomy ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1905
Author(s):  
Sanai Li ◽  
David Fleisher ◽  
Dennis Timlin ◽  
Vangimalla R. Reddy ◽  
Zhuangji Wang ◽  
...  
Keyword(s):  
Decision Support ◽  
Mississippi Delta ◽  
Rice Yield ◽  
Growing Season ◽  
The United States ◽  
Delta Region ◽  
Growth Duration ◽  
Cold Temperatures ◽  
Crop Models ◽  
General Response

The United States is one of the top rice exporters in the world, but warming temperatures and other climate trends may affect grain yield and quality. The use of crop models as decision support tools for a climate impact assessment would be beneficial, but suitability of models for representative growing conditions need to be verified. Therefore, the ability of CERES-Rice and ORYZA crop models to predict rice yield and growing season duration in the Mississippi Delta region was assessed for two widely-grown varieties using a 34-year database. CERES-Rice simulated growth duration more accurately than ORYZA as a result of the latter model’s use of lower cardinal temperatures. An increase in base and optimal temperatures improved ORYZA accuracy and reduced systematic error (e.g., correlation coefficient increased by 0.03–0.27 and root mean square error decreased by 0.3–1.9 days). Both models subsequently showed acceptable skill in reproducing the growing season duration and had similar performance for predicting rice yield for most locations and years. CERES-Rice predictions were more sensitive to years with lower solar radiation, but neither model accurately mimicked negative impacts of very warm or cold temperatures. Both models were shown to reproduce 50% percentile yield trends of more than 100 varieties in the region for the 34-year period when calibrated with two representative cultivars. These results suggest that both models are suitable for exploring the general response of multiple rice cultivars in the Mississippi Delta region for decision support studies involving the current climate. The response of rice growth and development to cold injury and high temperature stress, and variation in cultivar sensitivity, should be further developed and tested for improved decision making tools.

scholarly journals Analysis of the Effects of High Precipitation in Texas on Rainfed Sorghum Yields

Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1920 ◽  
Author(s):  
Sharma ◽  
Kannan ◽  
Cook ◽  
Pokhrel ◽  
McKenzie
Keyword(s):  
Crop Yield ◽  
Missing Values ◽  
Crop Yields ◽  
Principal Component ◽  
Growing Season ◽  
Grain Sorghum ◽  
Extreme Weather Events ◽  
Total Precipitation ◽  
Independent Variables ◽  
Two Parameters

Most of the recent studies on the consequences of extreme weather events on crop yields are focused on droughts and warming climate. The knowledge of the consequences of excess precipitation on the crop yield is lacking. We attempted to fill this gap by estimating reductions in rainfed grain sorghum yields for excess precipitation. The historical grain sorghum yield and corresponding historical precipitation data are collected by county. These data are sorted based on length of the record and missing values and arranged for the period 1973–2003. Grain sorghum growing periods in the different parts of Texas is estimated based on the east-west precipitation gradient, north-south temperature gradient, and typical planting and harvesting dates in Texas. We estimated the growing season total precipitation and maximum 4-day total precipitation for each county growing rainfed grain sorghum. These two parameters were used as independent variables, and crop yields of sorghum was used as the dependent variable. We tried to find the relationships between excess precipitation and decreases in crop yields using both graphical and mathematical relationships. The result were analyzed in four different levels; 1. Storm by storm consequences on the crop yield; 2. Growing season total precipitation and crop yield; 3. Maximum 4-day precipitation and crop yield; and 4. Multiple linear regression of independent variables with and without a principal component analysis (to remove the correlations between independent variables) and the dependent variable. The graphical and mathematical results show decreases in rainfed sorghum yields in Texas for excess precipitation could be between 18% and 38%.

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