Prediction of Crop Yield for New Mexico Based on Climate and Remote Sensing Data for the 1920–2019 Period

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1389
Author(s):  
Kamini Yadav ◽  
Hatim M. E. Geli
Keyword(s):  
Climate Change ◽  
New Mexico ◽  
Crop Yield ◽  
Crop Yields ◽  
Production Systems ◽  
Remotely Sensed ◽  
Adaptation Strategies ◽  
Yield Prediction ◽  
Climate Variables ◽  
Remotely Sensed Data

Agricultural production systems in New Mexico (NM) are under increased pressure due to climate change, drought, increased temperature, and variable precipitation, which can affect crop yields, feeds, and livestock grazing. Developing more sustainable production systems requires long-term measurements and assessment of climate change impacts on yields, especially over such a vulnerable region. Providing accurate yield predictions plays a key role in addressing a critical sustainability gap. The goal of this study is the development of effective crop yield predictions to allow for a better-informed cropland management and future production potential, and to develop climate-smart adaptation strategies for increased food security. The objectives were to (1) identify the most important climate variables that significantly influence and can be used to effectively predict yield, (2) evaluate the advantage of using remotely sensed data alone and in combination with climate variables for yield prediction, and (3) determine the significance of using short compared to long historical data records for yield prediction. This study focused on yield prediction for corn, sorghum, alfalfa, and wheat using climate and remotely sensed data for the 1920–2019 period. The results indicated that the use of normalized difference vegetation index (NDVI) alone is less accurate in predicting crop yields. The combination of climate and NDVI variables provided better predictions compared to the use of NDVI only to predict wheat, sorghum, and corn yields. However, the use of a climate only model performed better in predicting alfalfa yield. Yield predictions can be more accurate with the use of shorter data periods that are based on region-specific trends. The identification of the most important climate variables and accurate yield prediction pertaining to New Mexico’s agricultural systems can aid the state in developing climate change mitigation and adaptation strategies to enhance the sustainability of these systems.

scholarly journals Influence of Climate Change on Food Crop Yield in Benin Republic

2019 ◽  
Vol 11 (5) ◽  
pp. 281 ◽  
Author(s):  
Fèmi E. Hounnou ◽  
Houinsou Dedehouanou ◽  
Afio Zannou ◽  
Sofwaan Bakary ◽  
Elisée F. Mahoussi
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Crop Yields ◽  
Potential Evapotranspiration ◽  
Least Square ◽  
Irrigated Agriculture ◽  
Climate Variables ◽  
Staple Food ◽  
Food Crop ◽  
The Mean

World climate is projected to be more harmful and unforeseeable. A threefold combination of temperature, precipitation and potential evapotranspiration leads to climate change with a negative effect on staple food crop production. To understand the sensitivity of staple food crop yield to future change in climate, this paper uses the feasible generalized least square (FGLS) and heteroskedasticity and autocorrelation (HAC) consistent standard error techniques function to quantify the effects of climate variables on the mean and variance of crop yields. Data from FAOSTAT website and national institutions such as temperature, precipitation and crop areas cultivated for period 1961-2015 for Benin country are used. Climate variables are computed according to each crop growing season. The results showed that climate change could significantly influence the mean crop yields and could significantly affect the crop yield variability. The contribution of climate variables to crop yield varies across staple crop yields and they were predicted to decrease about 2025. In order to ensure food availability in the context of climate change, support to agricultural sector and especially to staple food crops production should be focused on seeds improvement by generating, developing and extending drought and flood-tolerant varieties. The results also implicate the promoting of irrigated agriculture.

scholarly journals Assessing the Sensitivity of Main Crop Yields to Climate Change Impacts in China

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 172
Author(s):  
Yuan Xu ◽  
Jieming Chou ◽  
Fan Yang ◽  
Mingyang Sun ◽  
Weixing Zhao ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Food Production ◽  
Crop Yields ◽  
Climatic Factors ◽  
Climatic Data ◽  
The South ◽  
The North ◽  
Output Elasticity ◽  
The Impact

Quantitatively assessing the spatial divergence of the sensitivity of crop yield to climate change is of great significance for reducing the climate change risk to food production. We use socio-economic and climatic data from 1981 to 2015 to examine how climate variability led to variation in yield, as simulated by an economy–climate model (C-D-C). The sensitivity of crop yield to the impact of climate change refers to the change in yield caused by changing climatic factors under the condition of constant non-climatic factors. An ‘output elasticity of comprehensive climate factor (CCF)’ approach determines the sensitivity, using the yields per hectare for grain, rice, wheat and maize in China’s main grain-producing areas as a case study. The results show that the CCF has a negative trend at a rate of −0.84/(10a) in the North region, while a positive trend of 0.79/(10a) is observed for the South region. Climate change promotes the ensemble increase in yields, and the contribution of agricultural labor force and total mechanical power to yields are greater, indicating that the yield in major grain-producing areas mainly depends on labor resources and the level of mechanization. However, the sensitivities to climate change of different crop yields to climate change present obvious regional differences: the sensitivity to climate change of the yield per hectare for maize in the North region was stronger than that in the South region. Therefore, the increase in the yield per hectare for maize in the North region due to the positive impacts of climate change was greater than that in the South region. In contrast, the sensitivity to climate change of the yield per hectare for rice in the South region was stronger than that in the North region. Furthermore, the sensitivity to climate change of maize per hectare yield was stronger than that of rice and wheat in the North region, and that of rice was the highest of the three crop yields in the South region. Finally, the economy–climate sensitivity zones of different crops were determined by the output elasticity of the CCF to help adapt to climate change and prevent food production risks.

scholarly journals Flood Risk and Adaptation Strategies for Soybean Production Systems on the Flood-Prone Pampas under Climate Change

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1187
Author(s):  
Wouter Julius Smolenaars ◽  
Spyridon Paparrizos ◽  
Saskia Werners ◽  
Fulco Ludwig
Keyword(s):  
Climate Change ◽  
Flood Risk ◽  
Risk Perceptions ◽  
Production Systems ◽  
Adaptation Strategies ◽  
Flood Events ◽  
Climate Data ◽  
Soybean Production ◽  
Drying Trend ◽  
Sowing Season

In recent decades, multiple flood events have had a devastating impact on soybean production in Argentina. Recent advances suggest that the frequency and intensity of destructive flood events on the Argentinian Pampas will increase under pressure from climate change. This paper provides bottom-up insight into the flood risk for soybean production systems under climate change and the suitability of adaptation strategies in two of the most flood-prone areas of the Pampas region. The flood risk perceptions of soybean producers were explored through interviews, translated into climatic indicators and then studied using a multi-model climate data analysis. Soybean producers perceived the present flood risk for rural accessibility to be of the highest concern, especially during the harvest and sowing seasons when heavy machinery needs to reach soybean lots. An analysis of climatic change projections found a rising trend in annual and harvest precipitation and a slight drying trend during the sowing season. This indicates that the flood risk for harvest accessibility may increase under climate change. Several adaptation strategies were identified that can systemically address flood risks, but these require collaborative action and cannot be undertaken by individual producers. The results suggest that if cooperative adaptation efforts are not made in the short term, the continued increase in flood risk may force soybean producers in the case study locations to shift away from soybean towards more robust land uses.

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.

The optical behaviour of snow during a melting season at Ny Ålesund (Svalbard, Norway)

2021 ◽  
Author(s):  
Roberto Salzano ◽  
Christian Lanconelli ◽  
Giulio Esposito ◽  
Marco Giusto ◽  
Mauro Montagnoli ◽  
...  
Keyword(s):  
Climate Change ◽  
Snow Cover ◽  
Full Range ◽  
Time Lapse ◽  
Test Site ◽  
Remotely Sensed ◽  
Optical Behaviour ◽  
Remotely Sensed Data ◽  
Snow Cover Area ◽  
Significant Information

<p><span>Polar areas are the most sensitive targets of </span><span>the </span><span>climate change and the continuous monitoring of the cryosphere represents a critical issue. The satellite remote sensing can fill this gap but further integration between remotely-sensed multi-spectral images and field data is crucial to validate retrieval algorithms and climatological models. The optical behaviour of snow, at different wavelengths, provides significant information about the micro-physical characteristics of the surface and this allow to discriminate different snow/ice covers. The aim of this work is to present an approach based on combining unmanned observations on spectral albedo and on the analysis of time-lapse images of sky and ground conditions in a</span><span>n </span><span>Ar</span><span>c</span><span>tic </span><span>test-site </span><span>(Svalbard, Norway). Terrestrial photography can provide, in fact, important information about the cloud cover and support the discrimination between white-sky or clear-sky illuminating conditions. Similarly, time-lapse cameras can provide a detailed description of the snow cover, estimating the fractional snow cover area. The spectral albedo was obtained by a narrow band device that was compared to a full-range commercial system and to remotely sensed data acquired during the 2015 spring/summer period at the </span><span>Amundsen - Nobile</span><span> Climate Change Tower (Ny </span><span>Å</span><span>lesund). The results confirmed the possibility to have continuous observations of the snow surface (microphisical) characteristics and highlighted the opportunity to monitor the spectral variations of snowed surfaces during the melting period. It was possible, </span><span>therefore,</span><span> to estimate spectral indexes, such as NDSI and SWIR albedo, and to found interesting links between both features and air/ground temperatures, wind-speed and precipitations. Different melting phases were detected and different processes were associated with the observed spectral variations.</span></p>

Future global crop production increases by adapting crop phenology to local climate change

2021 ◽  
Author(s):  
Sara Minoli ◽  
Jonas Jägermeyr ◽  
Senthold Asseng ◽  
Christoph Müller
Keyword(s):  
Climate Change ◽  
Crop Production ◽  
Management Practices ◽  
Crop Yields ◽  
Crop Productivity ◽  
Global Scale ◽  
Adaptation Strategies ◽  
Systematic Evaluation ◽  
Management Scenarios ◽  
Crop Phenology

<p>Broad evidence is pointing at possible adverse impacts of climate change on crop yields. Due to scarce information about farming management practices, most global-scale studies, however, do not consider adaptation strategies.</p><p>Here we integrate models of farmers' decision making with crop biophysical modeling at the global scale to investigate how accounting for adaptation of crop phenology affects projections of future crop productivity under climate change. Farmers in each simulation unit are assumed to adapt crop growing periods by continuously selecting sowing dates and cultivars that match climatic conditions best. We compare counterfactual management scenarios, assuming crop calendars and cultivars to be either the same as in the reference climate – as often assumed in previous climate impact assessments – or adapted to future climate.</p><p>Based on crop model simulations, we find that the implementation of adapted growing periods can substantially increase (+15%) total crop production in 2080-2099 (RCP6.0). In general, summer crops are responsive to both sowing and harvest date adjustments, which result in overall longer growing periods and improved yields, compared to production systems without adaptation of growing periods. Winter wheat presents challenges in adapting to a warming climate and requires region-specific adjustments to pre and post winter conditions. We present a systematic evaluation of how local and climate-scenario specific adaptation strategies can enhance global crop productivity on current cropland. Our findings highlight the importance of further research on the readiness of required crop varieties.</p>

scholarly journals Modeling the Impacts of Climate Change on Crop Yield and Irrigation in the Monocacy River Watershed, USA

Climate ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 139
Author(s):  
Manashi Paul ◽  
Sijal Dangol ◽  
Vitaly Kholodovsky ◽  
Amy R. Sapkota ◽  
Masoud Negahban-Azar ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Crop Yields ◽  
Swat Model ◽  
Global Climate Models ◽  
Climate Projections ◽  
Climate Change Scenarios ◽  
River Watershed ◽  
Temperature And Precipitation ◽  
The Impact

Crop yield depends on multiple factors, including climate conditions, soil characteristics, and available water. The objective of this study was to evaluate the impact of projected temperature and precipitation changes on crop yields in the Monocacy River Watershed in the Mid-Atlantic United States based on climate change scenarios. The Soil and Water Assessment Tool (SWAT) was applied to simulate watershed hydrology and crop yield. To evaluate the effect of future climate projections, four global climate models (GCMs) and three representative concentration pathways (RCP 4.5, 6, and 8.5) were used in the SWAT model. According to all GCMs and RCPs, a warmer climate with a wetter Autumn and Spring and a drier late Summer season is anticipated by mid and late century in this region. To evaluate future management strategies, water budget and crop yields were assessed for two scenarios: current rainfed and adaptive irrigated conditions. Irrigation would improve corn yields during mid-century across all scenarios. However, prolonged irrigation would have a negative impact due to nutrients runoff on both corn and soybean yields compared to rainfed condition. Decision tree analysis indicated that corn and soybean yields are most influenced by soil moisture, temperature, and precipitation as well as the water management practice used (i.e., rainfed or irrigated). The computed values from the SWAT modeling can be used as guidelines for water resource managers in this watershed to plan for projected water shortages and manage crop yields based on projected climate change conditions.

scholarly journals What Is the Consensus from Multiple Conclusions of Future Crop Yield Changes Affected by Climate Change in China?

2020 ◽  
Vol 17 (24) ◽  
pp. 9241
Author(s):  
Chengfang Huang ◽  
Ning Li ◽  
Zhengtao Zhang ◽  
Yuan Liu ◽  
Xi Chen ◽  
...  
Keyword(s):  
Climate Change ◽  
Crop Yield ◽  
Crop Yields ◽  
Positive Impact ◽  
Scientific Evidence ◽  
Future Climate Change ◽  
Systematic Review Methodology ◽  
Uncertainty Interval ◽  
The Impact ◽  
Yield Decrease

Many studies have shown that climate change has a significant impact on crop yield in China, while results have varied due to uncertain factors. This study has drawn a highly consistent consensus from the scientific evidence based on numerous existing studies. By a highly rational systematic review methodology, we obtained 737 result samples with the theme of climate change affecting China’s crop yields. Then, we used likelihood scale and trend analysis methods to quantify the consensus level and uncertainty interval of these samples. The results showed that: (i) The crop yield decrease in the second half of the 21st century will be greater than 5% of that in the first half. (ii) The crop most affected by climate change will be maize, with the decreased value exceeding −25% at the end of this century, followed by rice and wheat exceeding −10% and −5%. (iii) The positive impact of CO2 on crop yield will change by nearly 10%. Our conclusions clarify the consensus of the impact of future climate change on China’s crop yield, and this study helps exclude the differences and examine the policies and actions that China has taken and should take in response to climate change.

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