Model Comparisons Between Canonical Vine Copulas and Meta-Gaussian for Agricultural Drought Forecasting over China

Agricultural drought is caused by reduced soil moisture and precipitation and affects the growth of crops and vegetation, and in turn agricultural production and food security. For developing measures for drought mitigation, reliable agricultural drought forecasting is essential. In this study, we developed an agricultural drought forecasting model based on canonical vine copulas under three-dimensions (3C-vine model), in which the antecedent meteorological drought and agricultural drought persistence were utilized as predictors. Besides, the meta-Gaussian (MG) model was selected as a reference model to evaluate the forecast skill. The agricultural drought in August of 2018 was selected as a case study, and the spatial patterns of 1–3-month lead forecasts of agricultural drought utilizing the 3C-vine model resembled the corresponding observations, indicating the predictive ability of the model. The performance metrics (NSE, R2, and RMSE) showed that the 3C-vine model outperformed the MG model for August under diverse lead times. Also, the 3C-vine model exhibited excellent forecast skills in capturing the extreme agricultural drought over different selected typical regions. This study may help with drought early warning, drought mitigation, and water resources scheduling.

Resilient rainfed technologies for drought mitigation and sustainable food security

Even though drought is one of the most common features affecting rainfed agriculture, it is necessary to consider it as an extreme climatological event that requires different types of alleviating strategies for overcoming it. The risk involved in successful cultivation of crops depends on the nature of drought (chronic and contingent), its probable duration, and frequency of occurrence within the season. These aberrations are expected to further increase in future. A significant fall in food production is often noticed with increase in intensity or extension in duration of drought prevalence. Drought affects not only the food production at farm level but also the national economy and overall food security. Location-specific rainfed technologies are available to cope with different drought situations. Much of the research done in rainfed agriculture in India relates to conservation of soil & rainwater and to drought proofing. The key technologies for drought mitigation are in situ moisture conservation, rainwater harvesting and recycling, resilient crops and cropping systems including contingency crop plans, foliar sprays, and integrated farming systems. However, drought preparedness and real time implementation of contingency measures at field level needs well structured institutional support for farmers with strong government policy and convergence among various institutions. Ministry of Agriculture, Government of India, needs to facilitate the convergence process of various government schemes such as MGNREGA, RKVY, Mega Seed Project, NFSM, NHM, IWMP, Soil health schemes etc. for drought preparedness. National Mission for Sustainable Agriculture (NMSA), one among the missions under the Prime Minister National Action Plan for Climate Change (NAPCC) may take a lead role in implementation of contingency, by inclusion of this activity in State Action Plans (SAP) with a dedicated Nodal Institution /officers and budget provision.

Drought mitigation in cocoa (Theobroma cacao L.) through developing tolerant hybrids

Background Cocoa, being a shade loving crop cannot withstand long periods of water stress. Breeding for drought tolerance is the need of the hour due to change in climatic condition and extension of crop to non-traditional areas. Hybrids were produced by crossing four tolerant genotypes in all possible combination. The cross GV1 55 x M 13.12 didn’t yield any fruit due to cross incompatibility between these genotypes. Various biochemical parameters act as the true indicators to select tolerant and susceptible types. The major biochemical parameters considered after imposing stress included proline, nitrate reductase activity, superoxide dismutase content and glycine betaine. Results The drought tolerant hybrids were having high amount of proline, superoxide dismutase enzyme and glycine betaine content. Normally, plants having drought stress show low amount of nitrate reductase activity. However, in case of hybrids, the drought tolerant hybrids were having higher NR activity than the susceptible hybrids. The highest amount of NR was found in the control plants kept at fully irrigated conditions. Conclusions This experiment showed the role of different biochemical enzymes and osmolytes in giving tolerance to plants during drought stress. Logistic regression analysis selected proline and nitrate reductase as the two biochemical markers for identifying efficient drought tolerant genotypes in the future breeding programmes.

282 Nutritional Monitoring of Prenatally Stressed and Translocated Brahman Heifers

One drought mitigation strategy is transporting livestock to non-drought locations. Our objective was to evaluate the effects of prenatal stress and translocation on growing Bos indicus heifers. Twelve heifers born in spring 2019 at Overton, TX (1245 mm annual precipitation) were transported ~700 km to Sonora, TX (610 mm annual precipitation) in April 2020. Six heifers (283±10 kg) were born to dams subjected to transportation stress during mid-gestation (PNS) and 6 (279±17 kg) were born to non-stressed dams (CON). Heifers grazed a series of 24-ha native range pastures (aboveground forage biomass; 1508±390 kg/ha) and were sampled (non-shrunk BW/BCS score, feces) at 2-wk intervals from May through September. Fecal samples were collected from the ground or the rectum of each animal and stored at -20o C until processed for near infrared spectroscopy (NIRS) and prediction of diet crude protein (CP) and digestible organic matter (DOM). Inputs to a grazing animal nutrition model for prediction of BW included diet CP and DOM, age, and weather. Differences between groups for BW and nutritional parameters were determined by analysis of variance or paired t-test. Both groups gained BW (22±4 kg) throughout the study, there were no differences (P > 0.1) due to treatment. Diet CP and DOM were affected by date (P < 0.01) as diet quality declined from spring to fall. Percent diet CP was greater (P < 0.05) in PNS than CON, especially during July and August (6.94±0.10 vs 6.23±0.17, respectively). Corresponding values for diet DOM were (59.53±0.55 vs 59.14±0.43, respectively; P = 0.09). Observed vs model-predicted weight was different (P < 0.05) for both groups when using CP-based outputs, but not when using metabolizable protein-based outputs (P > 0.1). In summary, PNS heifers selected a diet of greater CP than CON, and tended to select a diet greater in DOM.

Perennial Crop Dynamics May Affect Long-Run Groundwater Levels

During California’s severe drought from 2011 to 2017, a significant shift in irrigated area from annual to perennial crops occurred. Due to the time requirements associated with bringing perennial crops to maturity, more perennial acreage likely increases the opportunity costs of fallowing, a common drought mitigation strategy. Increases in the costs of fallowing may put additional pressure on another common “go-to” drought mitigation strategy—groundwater pumping. Yet, overdrafted groundwater systems worldwide are increasingly becoming the norm. In response to depleting aquifers, as evidenced in California, sustainable groundwater management policies are being implemented. There has been little modeling of the potential effect of increased perennial crop production on groundwater use and the implications for public policy. A dynamic, integrated deterministic model of agricultural production in Kern County, CA, is developed here with both groundwater and perennial area by vintage treated as stock variables. Model scenarios investigate the impacts of surface water reductions and perennial prices on land and groundwater use. The results generally indicate that perennial production may lead to slower aquifer draw-down compared with deterministic models lacking perennial crop dynamics, highlighting the importance of accounting for the dynamic nature of perennial crops in understanding the co-evolution of agricultural and groundwater systems under climate change.

Is Drought Caused by Fate? Analysis of Farmers’ Perception and Its Influencing Factors in the Irrigation Areas of GAP-Şanlıurfa, Turkey

This research aims to determine the belief-based drought perceptions and attitudes of farmers in Şanlıurfa, which is in a semi-arid climate regime, and the factors affecting them. The surveys were conducted through face-to-face interviews with farmers selected by a simple random sampling method in 2020. Analyses were performed with ordinal logit regression in STATA. According to the results, while the effects of settlement location, land size, age, and the size of the household were statistically significant to farmers seeing drought, which is the dependent variable, as caused by fate, the effects of income, experience, and education level were insignificant. For the probability of predicting drought for each independent variable in the sequence analysis, the highest probabilities were found among farmers in the Harran Plain, with 21–30 years of experience, from a household of one to four people, with the land area between 5.1 and 10.0 hectares, aged 61 and above, who were primary school graduates, and who had an annual income of less than 25,000 TL ($3561). The subject of drought should be given more place in religious education in the entire research area by prioritizing these groups. It would also be beneficial to organize workshops for the farmers by agricultural consultants, where Islamic scholars would be present to support science and knowledge in terms of faith. This study is the first in this context in Turkey and provides useful data to policymakers for drought-mitigation policies.

Projected Drought Conditions over Southern Slope of the Central Himalaya Using CMIP6 Models

Nepal is located on the southern slope of the Central Himalayas and has experienced frequent droughts in the past. In this study, we used an ensemble of 13 biased corrected models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to assess the future drought conditions over Nepal under three shared socioeconomic pathways (SSP126, SSP245, and SSP585) using the Standardized Precipitation Evapotranspiration Index (SPEI) at annual timescale. The monthly correlation between observed and CMIP6-simulated historical SPEI is 0.23 (p < 0.01), which indicates the CMIP6 model ensemble can simulate the drought characteristics over Nepal. In the future period (2020–2100), the duration and severity of droughts are projected to increase with higher emission scenarios, especially for SSP585. Our results indicate enhanced drought intensity under SSP126, whereas, under SSP245, the drought frequency will be slightly higher. The drought frequency is projected to increase in the early future (2020–2060), decreasing in the late future (2061–2100) under all SSP scenarios. The results further indicate more prolonged and severe droughts in the early future under SSP585 as compared to SSP126 and SSP245. The findings of the present study can help drought mitigation as well as long-term adaptation strategies over Nepal.