Quantifying water and CO2 fluxes and water use efficiencies across irrigated C3 and C4 crops in a humid climate

Do rainfed production systems have lower environmental impact over irrigated production systems?: On -farm mitigation strategies

The intensive agriculture practices improved the crop productivity but escalated energy inputs (EI) and carbon foot print (CF) which contributes to global warming. Hence designing productive, profitable crop management practices under different production systems with low environmental impact (EI and CF) is the need of the hour. To identify the practices, quantification of baseline emissions and the major sources of emissions are required. Indian agriculture has diversified crops and production systems but there is dearth of information on both EI and CF of these production systems and crops. Hence the present study was an attempt to find hot spots and identify suitable strategies with high productivity, energy use efficiency (EUE) and carbon use efficiency (CUE). Energy and carbon balance of castor, cotton, chickpea, groundnut, maize, rice (both rainfed and irrigated), wheat, sugarcane (only irrigated), pigeon pea, soybean, sorghum, pearl millet (only rainfed) in different production systems was assessed. Field specific data on different crop management practices as well as grain and biomass yields were considered. Rainfed production systems had lower EI and CF than irrigated system. The nonrenewable sources of energy like fertilizer (64 %), irrigation (78 %), diesel fuel (75 %) and electricity (67 %) are the major source of energy input. Rainfed crops recorded higher CUE over irrigated condition. Adoption of technologies like efficient irrigation strategies (micro irrigation), enhancing fertilizer use efficiency (site specific nutrient management or slow release fertilizer), conservation agriculture (conservation or reduced tillage) rice cultivation methods (SRI or Direct seeded rice) were the mitigation strategies. These results will help policy makers and stake holders in adoption of suitable strategies for sustainable intensification.

Construction of a new LED chamber to measure net ecosystem exchange in low vegetation and validation study in grain crops

A vegetation canopy chamber system measures gas exchanges in the field between plants and the environment. Transparent closed chambers have generally been used to measure canopy fluxes in the field, depending on solar radiation as the light source for photosynthesis. However, measuring canopy fluxes in nature can be challenging due to fluctuations in solar radiation. Therefore, we constructed a novel transient-state closed-chamber system using light-emitting diodes (LEDs) as a light source to measure canopy-scale fluxes. The water-cooled chamber system used a 1600 Watt LED module to produce constant photosynthetically active radiation (PAR) and a CO₂ gas analyzer for concentration measurements. We used the LED chamber system to measure barley and wheat gas exchanges in the field to quantify CO₂ fluxes along a PAR gradient. This novel technology enables the determination of photosynthesis rates for various crops under diverse environmental conditions, in diverse ecosystems, and across long-term interannual changes, including those due to climate change.

CO2 flux in a wheat-soybean succession in subtropical Brazil: A carbon sink

The subtropical region of Brazil is home to 33% of the soybean [Glycine max (L.) Merr.] growing area and 90% of the wheat (Tritucum aestivum L.) growing area of this country. A soybean-wheat succession with fallow between crops is used in about 11% of the cultivated area. No study has quantified CO2 fluxes in annual soybean-wheat succession in this region. Hence, this study analyzed the seasonality of CO2 exchange (net ecosystem exchange [NEE]) in a 2015/2016 wheat-soybean succession in a commercial farm located in Carazinho, Rio Grande do Sul State, Brazil. The eddy covariance method was used to estimate the annual C balance of this system. The NEE was partitioned between gross primary productivity and ecosystem respiration to understand the dynamics of these fluxes during a year of wheat-soybean succession. Considering the net ecosystem balance between photosynthesis and respiration during the growing season, both soybean and wheat absorbed CO2 from the atmosphere (NEE wheat: -347 ± 4 g C m-2 ; NEE soybean: -242 ± 3 g C m-2 ). The fallow periods between growing seasons, however, acted as a source of 156 ± 2 g C m-2 , reducing the C absorbed by the crops by 27%. For 1 yr, the net biome productivity was -50 g C m-2  yr-1 . The results obtained here demonstrate that the wheat-soybean succession was a net C sink under these specific climatic conditions and field management practices and that the long fallow period between crops limited the agroecosystem from becoming a more efficient CO2 sink.

Study on the Water and Heat Fluxes of a Very Humid Forest Ecosystem and Their Relationship with Environmental Factors in Jinyun Mountain, Chongqing

The high-humidity mountain forest ecosystem (HHMF) of Jinyun Mountain in Chongqing is a fragile ecosystem that is sensitive to climate change and human activities. Because it is shrouded in fog year-round, illumination in the area is seriously insufficient. However, the flux (energy, water) exchanges (FEs) in this ecosystem and their influencing factors are not clear. Using one-year data from flux towers with a double-layer (25 m and 35 m) eddy covariance (EC) observation system, we proved the applicability of the EC method on rough underlying surfaces, quantified the FEs of HHMFs, and found that part of the fog might also be observed by the EC method. The observation time was separated from day and night, and then the environmental control of the FEs was determined by stepwise regression analysis. Through the water balance, it was proven that the negative value of evapotranspiration (ETN), which represented the water vapor input from the atmosphere to the ecosystem, could not be ignored and provided a new idea for the possible causes of the evaporation paradox. The results showed that the annual average daily sensible heat flux (H) and latent heat flux (LE) ranged from −126.56 to 131.27 W m−2 and from −106.7 to 222.27 W m−2, respectively. The annual evapotranspiration (ET), positive evapotranspiration (ETP), and negative evapotranspiration (ETN) values were 389.31, 1387.76, and −998.45 mm, respectively. The energy closure rate of the EC method in the ecosystems was 84%. Fog was the ETN observed by the EC method and an important water source of the HHMF. Therefore, the study area was divided into subtropical mountain cloud forests (STMCFs). Stepwise regression analysis showed that the H and LE during the day were mainly determined by radiation (Rn) and temperature (Tair), indicating that the energy of the ecosystem was limited, and future climate warming may enhance the FEs of the ecosystem. Additionally, ETN was controlled by wind speed (WS) in the whole period, and WS was mainly affected by altitude and temperature differences within the city. Therefore, fog is more likely to occur in the mountains near heat island cities in tropical and subtropical regions. This study emphasizes that fog, as an important water source, is easily ignored in most EC methods and that there will be a large amount of fog in ecosystems affected by future climate warming, which can explain the evaporation paradox.

Effects of clouds and aerosols on ecosystem exchange, water and light use efficiency in a humid region orchard

Investigating the patterns of water and carbon dynamics in agro-ecosystems in response to clouds and aerosols can shed new insights in understanding the biophysical impacts of climate change on crop productivity and water consumption. In this study, the effects of clouds and aerosols as well as other environmental factors on ecosystem water and carbon fluxes were examined based on three-year eddy covariance measurements under different sky conditions (quantified as the clearness index, K_t, i.e., the ratio of global solar radiation to extraterrestrial solar radiation) in a kiwifruit plantation in the humid Sichuan Basin of China. Results showed that evapotranspiration (ET) and canopy transpiration (T_c, measured by sap flow sensors) increased, while ecosystem light use efficiency (eLUE) and ecosystem water use efficiency (eWUE) decreased with increasing K_t. GPP presented a parabolic relationship with increasing K_t. The path analysis revealed that surface conductance (G_s) and canopy conductance (G_c) were the most dominant variables directly regulated carbon (GPP) and water (ET and T_c) fluxes. The effect path of K_t on ET and T_c was converted from through diffuse photosynthetic active radiation (PAR_dif) to direct PAR (PAR_dir) when the sky became clearer. The effect path of K_t on GPP was primarily through PAR_dif under different sky conditions. The declined eWUE with increasing K_t was caused by the different responses of GPP and ET to PAR_dir under clear skies. The declined eLUE resulted from the sharp decrease in GPP/PAR_dir, which surpassed the slight increase of GPP/PAR_dif with increasing PAR. The Priestley-Taylor Jet Propulsion Laboratory ET model (PT-JPL) incorporating K_t with an exponential function produced more reliable T_c estimates but minor improvement in ET. Further, the LUE-GPP model incorporating K_t with a linear function obtained much better GPP estimates. Our study shed light on how sky conditions modulate water and carbon dynamics between the biosphere and atmosphere, highlighting the necessity of the inclusion of sky conditions for better modeling regional water and carbon budgets.

Estimating Gross Primary Productivity (GPP) over Rice–Wheat-Rotation Croplands by Using the Random Forest Model and Eddy Covariance Measurements: Upscaling and Comparison with the MODIS Product

Despite advances in remote sensing–based gross primary productivity (GPP) modeling, the calibration of the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product (GPPMOD) is less well understood over rice–wheat-rotation cropland. To improve the performance of GPPMOD, a random forest (RF) machine learning model was constructed and employed over the rice–wheat double-cropping fields of eastern China. The RF-derived GPP (GPPRF) agreed well with the eddy covariance (EC)-derived GPP (GPPEC), with a coefficient of determination of 0.99 and a root-mean-square error of 0.42 g C m−2 d−1. Therefore, it was deemed reliable to upscale GPPEC to regional scales through the RF model. The upscaled cumulative seasonal GPPRF was higher for rice (924 g C m−2) than that for wheat (532 g C m−2). By comparing GPPMOD and GPPEC, we found that GPPMOD performed well during the crop rotation periods but underestimated GPP during the rice/wheat active growth seasons. Furthermore, GPPMOD was calibrated by GPPRF, and the error range of GPPMOD (GPPRF minus GPPMOD) was found to be 2.5–3.25 g C m−2 d−1 for rice and 0.75–1.25 g C m−2 d−1 for wheat. Our findings suggest that RF-based GPP products have the potential to be applied in accurately evaluating MODIS-based agroecosystem carbon cycles at regional or even global scales.

Effects of human-induced land degradation on water and carbon fluxes in two different Brazilian dryland soil covers

The Brazilian semiarid region presents a physical water scarcity and high seasonal and interannual irregularities of precipitation, known as a region with periodic droughts. This region is mainly covered by the Caatinga biome, recognized as a Seasonally Dry Tropical Forest (SDTF). Soil water availability directly impacts the ecosystem's functioning, characterized by low fertility and sparse vegetation cover during the dry season, making it a fragile ecosystem vulnerable to climatic variations. Additionally, this region has been suffering from several issues due to human activities over the centuries, which has resulted in extensive areas being severely degraded, which aggravates the impacts from climatic variations and the susceptibility to desertification. Thus, studying the soil-plant-atmosphere continuum in this region can help better understand the seasonal and annual behavior of the water and carbon fluxes. This study investigated the dynamics of water and carbon fluxes during four years (2013-2016) by using eddy covariance (EC) measurements within two areas of Caatinga (dense Caatinga (DC) and sparse Caatinga (SC)) that suffered anthropic pressures. The two study areas showed similar behavior in relation to physical parameters (air temperature, incoming radiation, vapor pressure deficit, and relative humidity), except for soil temperature. The SC area presented a surface temperature of 3 °C higher than the DC, related to their vegetation cover differences. The SC area had higher annual evapotranspiration, representing 74% of the precipitation for the DC area and 90% for the SC area. The two areas acted as a carbon sink during the study period, with the SC area showing a lower CO₂ absorption capacity. On average, the DC area absorbs 2.5 times more carbon than the SC area, indicating that Caatinga deforestation affects evaporative fluxes, reducing atmospheric carbon fixation and influencing the ability to mitigate the effects of increased greenhouse gas concentrations in the atmosphere.

Predicting carbon and water vapor fluxes using machine learning and novel feature ranking algorithms

Gap-filling eddy covariance flux data using quantitative approaches has increased over the past decade. Numerous methods have been proposed previously, including look-up table approaches, parametric methods, process-based models, and machine learning. Particularly, the REddyProc package from the Max Planck Institute for Biogeochemistry and ONEFlux package from AmeriFlux have been widely used in many studies. However, there is no consensus regarding the optimal model and feature selection method that could be used for predicting different flux targets (Net Ecosystem Exchange, NEE; or Evapotranspiration -ET), due to the limited systematic comparative research based on the identical site-data. Here, we compared NEE and ET gap-filling/prediction performance of the least-square-based linear model, artificial neural network, random forest (RF), and support vector machine (SVM) using data obtained from four major row-crop and forage agroecosystems located in the subtropical or the climate-transition zones in the US. Additionally, we tested the impacts of different training-testing data partitioning settings, including a 10-fold time-series sequential (10FTS), a 10-fold cross validation (CV) routine with single data point (10FCV), daily (10FCVD), weekly (10FCVW) and monthly (10FCVM) gap length, and a 7/14-day flanking window (FW) approach; and implemented a novel Sliced Inverse Regression-based Recursive Feature Elimination algorithm (SIRRFE). We benchmarked the model performance against REddyProc and ONEFlux-produced results. Our results indicated that accurate NEE and ET prediction models could be systematically constructed using SVM/RF and only a few top informative features. The gap-filling performance of ONEFlux is generally satisfactory (R² = 0.39-0.71), but results from REddyProc could be very limited or even unreliable in many cases (R² = 0.01-0.67). Overall, SIRRFE-refined SVM models yielded excellent results for predicting NEE (R² = 0.46-0.92) and ET (R² = 0.74-0.91). Finally, the performance of various models was greatly affected by the types of ecosystem, predicting targets, and training algorithms; but was insensitive towards training-testing partitioning. Our research provided more insights into constructing novel gap-filling models and understanding the underlying drivers affecting boundary layer carbon/water fluxes on an ecosystem level.

Water Use Efficiencies of Different Maturity Group Soybean Cultivars in the Humid Mississippi Delta

Introducing alternative cultivars with enhanced water use efficiencies can help alleviate pressure on groundwater for crop irrigations in Mississippi (MS) Delta. A two-year field study was conducted in 2019–2020 to compare the water use efficiencies (WUE) of recently released and pre-released soybean {Glycine max (L.) Merr.} cultivars in maturity group (MG) III (‘P37A78’, ‘LG03-4561-14’), IV (‘Dyna-gro 4516x’, ‘DS25-1, DT97-4290’), and V (‘S12-1362’, ‘S14-16306’) in the MS Delta. The experimental design was a split-plot with cultivar as the first factor and the second factor was water variant irrigation (IR) and no irrigation (RF, rainfed), replicated three times. The MG IV cultivar Dyna-gro 4516x recorded the highest grain yield and WUE: grain yields were 4.58 Mg ha−1 and 3.89 Mg ha−1 under IR and RF, respectively in 2019, and 4.74 Mg ha−1 and 4.35 Mg ha−1 in 2020. The WUE were 7.2 and 6.9 kg ha−1 mm−1, respectively, in 2019 under IR and RF, and 13.4 and 16.9 kg ha−1 mm−1 in 2020. The data reveals that ‘Dyna-gro 4516x’ (MG IV), ‘LG03-4561-14’ (MG III), and ‘P37A78’ (MG III) are best adapted to the early soybean production system (ESPS) in MS Delta region for sustainable production for conserving water resources.

Impact of climate, rising atmospheric carbon dioxide, and other environmental factors on water-use efficiency at multiple land cover types

Rising atmospheric CO₂, changing climate, and other environmental factors such as nitrogen deposition and aerosol concentration influence carbon and water fluxes significantly. Water-use efficiency (WUE) was used to analyze these factors over 3 decades (1981–2010) using the Community Land Model 5.0 (CLM5.0). The study analyzes the effects of climate and other environmental factors on multiple land cover types (forest, grassland, and cropland) with divided study periods (1981–2000 and 2001–2010). Ecosystem WUE (EWUE) and transpiration WUE (TWUE) increased at the forest site due to the CO₂ fertilization effect but decreased at the grassland and cropland sites due to lower gross primary production and higher/lower (cropland/grassland) evapotranspiration as consequences of rising temperature and water availability. Inherent WUE confirmed that EWUE and TWUE trends were controlled by the rising temperature and CO₂-induced warming through an increase in vapor pressure deficit. In this way, forest and cropland sites showed warming patterns, while the grassland site showed a drier climate. The later period (2001–2010) showed steeper trends in WUE compared with the earlier period at all sites, implying a change in climate. The results showed implications for rising temperature due to increased CO₂ concentration at multiple land cover types.