Crop Planting Date Research Articles

Assessing agricultural vulnerability to climate change through dynamic indexing approach.

The present study aims to assess agricultural vulnerability in the context of climate change, focusing on the diverse districts of Odisha. Acknowledging that vulnerability is influenced by exposure, sensitivity, and adaptive capacity, our research incorporates the growth rate and instability of vital performance indicators to evaluate the relative vulnerability of each district. A key strength of this approach is the use of normalized indicators, weighted in accordance with the proportional acreage of major crops in each district relative to the state, culminating in a comprehensive vulnerability index through the aggregation of these weighted components. Our findings reveal significant variability in the vulnerability profiles across districts, thereby necessitating state-level intervention through tailored "Location Performance Vulnerability" based adaptation strategies. These strategies, including early weather warning systems, development of new and early maturing crop varieties, and adjustment of crop planting dates, are crucial for mitigating the adverse effects of climate change on agriculture. The study's methodology and findings offer significant contributions to the field, providing policymakers and stakeholders with a district-specific framework for climate change adaptation. This approach is especially relevant for the international academic and policy-making communities, as it highlights the importance of localized adaptation strategies in the broader context of global climate change resilience.

Smallholder farmers’ perceived motivations for the adoption and implementation of climate information services in the Atwima Nwabiagya District, Ghana

This study investigated smallholder farmers’ perceived motivations for the adoption and implementation of climate information services (CIS) in the Atwima Nwabiagya District, Ghana. Specifically, the study answered the following research questions. (i) What is the perception of smallholder farmers on climate change and variability in the Atwima Nwabiagya District?; (ii) What are the CIS accessed and used by the smallholder farmers for farm management decisions?; (iii) What are the perceived motivations for the adoption and implementation of CIS by smallholder farmers?; and; (iv) What are the barriers confronting smallholder farmers in adopting and implementing CIS? Primary data was collected through questionnaire surveys with 200 farming households and four key informant interviews in four selected communities (Kobeng, Seidi, Wioso, and Nkawie Panin) in the study district. Results showed that smallholder farmers perceived climate change and variability through increased windstorms and thunderstorms, variable rainfall, and increased temperature patterns. The surveyed farmers also perceived that the climatic changes affect their agricultural activities adversely. However, the smallholder farmers accessed and used CIS including weather warnings (86%), daily weather forecasts (70%), and seasonal forecasts (60%) to manage climatic risks on the farm. Results also showed that the smallholder farmers were motivated by critical factors including improvement in the acquisition of proactive measures (92%), increased knowledge of climatic patterns that affect food stock (91%), and a reduction in loss of stock produced (88%) to access and use CIS. Despite this, the smallholder farmers were confronted with key barriers in their uptake and utilization of CIS. These were the timeliness of CIS (73%), a lack of weather symbols in the forecasts (65%), and inadequate information on seasonal forecasts for long-term planning (61%). The study concluded that the motivations for smallholder farmers using CIS in the study district may vary depending on their context, goals, and challenges. However, the general motivation is to enhance smallholder farmers’ farm planning and risk management by using weather and climate forecasts to make better decisions on crop selection, planting dates, input use, pest and disease control, irrigation, harvesting, and marketing.

The Contribution of Climate-Smart Agriculture to Reducing Climate-Related Risks to Rain-Fed Maize Production: Insights from Tanzania’s Semi-Arid and Sub-Humid Regions

Adaptation responses of rain-fed smallholder farming systems to climate change and variability have become more unpredictable. Semi-arid and sub-humid regions are most affected by knowledge gaps on climate change adaptation strategies. Climate-smart agriculture (CSA) practices are crucial because they ensure predictable and effective adaptation responses that reduce crop failures. To determine potential new strategies for farm-level climate change adaptation, this study examined the implications of three CSA practices on rain-fed maize production: crop diversification, intercropping, and planting date adjustment. Plot-level data were obtained from the Tanzania National Panel Survey (TNPS) throughout waves 1 (2008/2009) and 2 (2010/2011). The consistency of the adaptation responses was evaluated using test-retest reliability. The findings showed that intercropping and splitting plots to plant crops other than maize was an analogue for crop diversification and reduced variability of maize grain yields. The panel linear regression model revealed that the yields of maize grains were positively correlated with intercropping and crop diversification (plot division). Moreover, the results of the meta-analysis showed that intercropping, crop diversification, and planting date adjustments could greatly increase smallholder rain-fed maize farmers’ resistance to the effects of climate variability and change. The government and non-governmentalorganizations should be encouraged to provide funding for agricultural extension education, which is a major factor in the implementation of CSA techniques.

Cover crop management strategies affect weeds and profitability of organic no-till soybean

Abstract Cover crop residue retention on the soil surface can suppress weeds and improve organic no-till soybean (Glycine max) yield and profitability compared to a tilled system. Appropriate cereal rye (Secale cereale) fall planting date and termination methods in the spring are critical to achieve these benefits. A plot-scale agronomic experiment was carried out from September 2018 to October 2021 in Kutztown, PA, USA to demonstrate the influence of cereal rye planting date (September or October) and mechanical termination method [no-till (I & J roller-crimper, Dawn ZRX roller, and mow-ted) and tilled (plow-cultivate)] on cover crop regrowth density, weed biomass, soybean yield, and economic returns. In one out of three years, the September rye planting accumulated more cover crop biomass than the October planting, but the regrowth of the rye after roller-crimping was greater with this planting date. Cover crop planting date had no effect on total weed biomass and demonstrated varying effects on soybean grain yield and economic returns. The Dawn ZRX roller outperformed the I & J roller-crimper in effectively terminating cover crops, while the I & J roller-crimper demonstrated more uniform weed suppression and led to greater soybean yields over a span of three years. Organic no-till strategies eliminated the need for tillage and reduced variable costs by 14% over plow-cultivated plots, and generated ~19% greater net revenue across the study period (no-till vs tillage = US $845 vs US $711 ha−1). Terminating cereal rye with roller-crimping technology can be a positive investment in an organic soybean production system.

Unveiling the Thirst: Revealing the Water Requirements of Gujrat's Thriving Crops using CROPWAT 8.0

Water plays an essential role in agriculture, serving as a primary reserve that directly impacts crop production and food security. Water conservation is critical for sustainable agriculture and the well-being of communities and ecosystems, chiefly due to the increasing global demand and rising water dearth concerns. The water needs of crops and irrigation scheduling in the district Gujrat, Pakistan, are currently missing enough information. Therefore, this study was conducted to determine the crop water requirements and establish irrigation schedules for the major crops in district Gujrat, Pakistan. The crop water requirement (CWR) and irrigation schedules were determined by CROPWAT 8.0 by using the input climate data obtained from the POWER-NASA website from 1991-2020. Other than climatic data, crops (wheat, rice, sugarcane, and maize) and soil information were added from CROPWAT 8.0 pre-existing data. The crop planting dates were estimated by Ayub Agriculture Research Institute (AARI). The gross and net crop water requirements are 624.3 mm and 491.3 mm for wheat, 1805.2 mm and 1768.3 mm for rice, 4135.1 mm and 3537.2 mm for sugarcane, and 1203.5 mm and 1051.4 mm for maize. The deployment of CROPWAT 8.0 proves to be valuable in precisely assessing crop water requirements and computing irrigation scheduling. The study's outcomes show the potential to boost food production and aid in well-organized water reserve management.

Observation-based sowing dates and cultivars significantly affect yield and irrigation for some crops in the Community Land Model (CLM5)

Abstract. Farmers around the world time the planting of their crops to optimize growing season conditions and choose varieties that grow slowly enough to take advantage of the entire growing season while minimizing the risk of late-season kill. As climate changes, these strategies will be an important component of agricultural adaptation. Thus, it is critical that the global models used to project crop productivity under future conditions are able to realistically simulate growing season timing. This is especially important for climate- and hydrosphere-coupled crop models, where the intra-annual timing of crop growth and management affects regional weather and water availability. We have improved the crop module of the Community Land Model (CLM) to allow the use of externally specified crop planting dates and maturity requirements. In this way, CLM can use alternative algorithms for future crop calendars that are potentially more accurate and/or flexible than the built-in methods. Using observation-derived planting and maturity inputs reduces bias in the mean simulated global yield of sugarcane and cotton but increases bias for corn, spring wheat, and especially rice. These inputs also reduce simulated global irrigation demand by 15 %, much of which is associated with particular regions of corn and rice cultivation. Finally, we discuss how our results suggest areas for improvement in CLM and, potentially, similar crop models.

Farmers’ perspectives on water availability in the lower Volta Delta region in Ghana

Smallholder farmers in the lower Volta Delta of Ghana operate in a risky and uncertain context due to water scarcity. However, the conventional analyses of water availability have mainly focused on drought, rainfall occurrence, and other climatic or weather elements, using secondary biophysical data with relatively little emphasis on farmers’ perceptions and how socioeconomic factors contribute to the vulnerability of farming in the lower Volta Delta. This study examines farmers’ perceptions of vulnerability to water availability to determine how perception is socially constructed and differentiated in various locations. The study was conducted using qualitative research methods in the Ada East, Ada West, South Tongu, and Anloga Districts in the lower Volta Delta of Ghana. Findings reveal that climate is not the only stressor on livelihoods in the area. Farmers perceive their livelihoods as vulnerable to socioeconomic stressors, including limited freshwater and groundwater resources. The perceived severity of water availability conditions determines farmers’ response strategies, such as crop selection, planting dates, irrigation strategies, and other agronomic practices. These findings have important implications for researchers, agricultural extension agents, and policymakers to design appropriate measures and approaches to deal with water scarcity for farming in the region. The study also contributes valuable insights into the loss and damage incurred by farmers due to climate change and variability in the region.

Corn and soybean planting order decisions impact farm gross revenue

AbstractThe inter‐annual corn (Zea mays L.)–soybean [Glycine max (L.) Merr.] rotation field is a well‐known management practice that increases the yield of both crops across the midwestern United States. Each spring, farmers must decide which crop will be planted first. Prioritizing the planting of one crop can delay planting of the other, which can result in substantial yield loss and reduced associated revenue. The objective of this work was to assess how gross farm revenue (corn + soybean acres) can be affected by crop planting order (corn first, soybean second, and vice versa). The impact of variable planting dates on the yield of each crop was simulated for 310 fields across the United States. Gross farm revenue was estimated as a function of crop planting date, order, input costs and crop prices. In a randomly chosen field in south central Wisconsin, 1 out of the 310, delaying planting after May 1 reduced yield of each crop and subsequently suppressed gross farm revenue. Crop planting order determined farm revenue due to a variable loss in per day yield rate within the nominal planting timeframe associated with the two crops. In addition, the degree to which management intensified for each crop relative to crop yield potential accruing with earlier planting varied by state and further impacted farm revenue. Overall results suggest that to determine planting order, US farmers need to be aware of the comparative yield trends associated with delayed planting of corn vs. soybean for their specific farms and cropping systems and should also account for projected crop selling prices.

CropSow: An integrative remotely sensed crop modeling framework for field-level crop planting date estimation

Crop planting timing is critical in regulating environmental conditions of crop growth throughout the season, and is an essential parameter in crop simulation models for estimating dry matter accumulation and yields. Accurate planting date information is key to characterizing crop growing dynamics under varying farming practices and facilitating agricultural adaptation to climate change. To date, the main methods to acquire planting dates include field survey methods, weather-dependent methods, and remote sensing phenological detecting methods. However, it is still challenging to effectively estimate the crop planting dates at field levels due to the lack of appropriate field-level modeling design as well as the dearth of ground planting reference data. In our study, we develop a novel CropSow modeling framework to estimate field-level planting dates by integrating the remote sensing phenological detecting method with the crop growth model. The remote sensing phenological detecting method is devised to retrieve the critical crop phenological metrics of farm fields from remote sensing time series, which are then integrated into the crop growth model for field planting date estimation in consideration of soil-crop-atmosphere continuum. CropSow leverages the rich physiological knowledge embedded in the crop growth model to scalably interpret satellite observations under a variety of environmental and management conditions for field-level planting date retrievals. With corn in Illinois, US as a case study, the developed CropSow outperforms three advanced benchmark models (i.e., the remote sensing accumulative growing degree day method, the weather-dependent method, and the shape model) in crop planting date estimation at the field level, with R square higher than 0.68, root mean square error (RMSE) lower than 10 days, and mean bias error (MBE) around 5 days from 2016 to 2020. It achieves better generalization performance than the benchmark models, as well as stronger adaptability to abnormal weather conditions with more robust performance in estimating the planting dates of farm fields. CropSow holds considerable promise to extrapolate over space and time for estimating the timing of crop planting of individual farm fields at large scales.

Field-scale dynamics of planting dates in the US Corn Belt from 2000 to 2020

Crop planting dates are a dynamic feature of agricultural systems that respond to short- and long-term climate signals, crop and cultivar selection, and technology changes. Planting date records are essential for yield gap analyses, accurate crop modeling, and tracking farmer adaptations to weather and climate change. Although planting dates have high variation at local scales due to heterogeneity in farm resources and decision-making, available long-term data on planting dates are largely restricted to aggregated regional statistics or, at best, satellite-derived datasets with limited spatiotemporal extent and at resolutions unable to distinguish individual fields. Here, we generated retrospective annual field-scale (30 m) planting date maps for both maize and soybeans spanning 2000–2020 across a 12-state region in the United States Corn Belt based on Landsat satellite data and a large ground sample of over 28,000 maize and soybean fields. Using training data from 2015 to 2020 for model selection, we found that planting date predictions improved with harmonic regression of Landsat data and additional annual weather covariates. The preferred random forest model approximately doubled performance compared to a null model based on state median planting dates, capturing 47% of field-level variation for maize (mean absolute error, MAE = 7.4 days) and 44% for soybeans (MAE = 7.5 days) against held-out ground truth test data for 2008–2014. We also evaluated the full 2000–2020 dataset with state agricultural statistics, finding strong agreement with median planting dates for maize (R2 = 0.76, MAE = 4.4 days) and slightly lower agreement for soybeans (R2 = 0.65, MAE = 5.4 days) when aggregated to the state level. We then used this new dataset to analyze environmental determinants of planting dates at a finer-scale than previously possible, controlling for unobserved variation at the sub-state district level. We found that during 2000–2020, each standard deviation increase in early-season rainfall delayed planting by ∼2.5 days, and fields with higher soil productivity ratings tended to be planted earlier. We did not find meaningful trends over the last two decades in planting dates for maize or soybeans, in contrast to trends towards earlier planting dates late last century and predicted for this period in climate adaptation studies. We hypothesize increases in early season rainfall or persisting early-season frost risks may have inhibited these shifts towards earlier planting. Remotely sensed planting dates will be a useful tool for yield gap analyses, crop simulation modeling, and ongoing assessment of climate adaptation.

Evaluating the impact of at-plant termination of a cereal rye cover crop with different corn planting dates on arthropod activity.

Greater cover crop biomass is expected to result in a favorable microhabitat for beneficial arthropods. Natural Resources Conservation Service (NRCS) cover crop termination guidelines are based on the cash crop planting date. Therefore, a delay in cash crop planting could result in greater cover crop biomass. However, studies on delays in cash crop planting and greater cover crop biomass have led to a decrease in cash crop yield. Thus, a field study was conducted in eastern Nebraska over two years to evaluate the impact of early and late corn planting dates with at-plant cover crop terminations on pest potential, beneficial arthropod activity, and agronomic parameters. To measure arthropod activity and pests in the system, pitfall traps, and corn injury assessments were performed during the early stages of corn development. A total of 11,054 and 43,078 arthropods were collected in 2020 and 2021, respectively. The results have shown no impact of the corn planting dates with at-plant cover crop termination on arthropods but identified that cereal rye cover crop supports greater Araneae activity while its alternative prey varied when compared to the no-cover treatment. Significant yield penalties were observed when cover crop was used regardless of the corn planting dates. Pest pressure was not significant in any year, however, future research using cereal rye and different cover crop species should be used in this system with an artificial infestation of a pest to be able to evaluate the trade-offs between possible cash crop yield reductions and potential biological control of pests.

Water quality benefits of weather-based manure application timing and manure placement strategies

The timing of manure application and placement of manure significantly affects manure nutrient use efficiency and the amount of nutrient lost from a field. Application of manure prior to a minimal precipitation period, and manure application through incorporation, reduces risks associated with nutrient loss through surface runoff. The current study aims to explore potential water quality impacts related to manure application strategies on the timing of application and approach (surface broadcasting or incorporation). The Soil and Water Assessment Tool (SWAT) was used to represent manure application scenarios and quantify potential water quality impacts in Susquehanna River Basin located in the Mid-Atlantic region of the United States. A baseline (business-as-usual) scenario was developed with manure application based on crop planting date and manure storage availability, and surface broadcasting as the application approach. The baseline was compared with a strategically timed manure application considering weather forecasting and manure incorporation. The strategic, weather-based manure application approach reduced TN and TP loading at the outlet by 4% and 6%, respectively. Manure incorporation simulations considering low-disturbance injection showed significant reduction of about 19% for TN and 44% for TP at the watershed outlet. Winter closure of manure application could reduce organic nutrient loss. Winter application of manure in 21% of row cropped areas (2% of whole watershed area) increased organic N and P loading by 10% and 4%, respectively, at watershed outlet.

Influence of rye/oat cover crop in a two‐year soybean production system

AbstractAs the cost of corn (Zea mays L.) production increases, some farmers find continuous soybean [Glycine max (L.) Merr.] to be more profitable than corn–soybean rotation. However, a grass cover crop could help diversify the cropping system. The study objectives were to evaluate planting date and termination timing of a rye (Secale cereale L.)/oat (Avena sativa L.) cover crop mix on cover crop biomass and soybean disease, insects, slugs, and grain yield within a 2‐yr soybean production system. Field trials were conducted in 2018 and 2019 at two locations in Ohio (northwestern and western). The main plot factor was cover crop planting date (September through October) and the subplot factor was cover crop termination timing (2 and 4 wk prior to soybean planting). Cover crop planting date and termination timing had little influence on soybean diseases and insects. Soybean grain yield was influenced by cover crop termination timing at the northwestern location. In 2018, soybean yield was 4% greater when the preceding cover crop was terminated late and in the control (no cover crop) compared to early cover crop termination. The following year, soybean yield was 8% greater when the cover crop was terminated late compared to early termination and the control. At the western location, there was no impact of cover crop termination timing on yield. Late‐terminated cover crops had a positive effect or no effect on yield of the following soybean crop.

Cold winters drive consistent and spatially synchronous 8-year population cycles of cabbage stem flea beetle.

Population cycles have been observed in mammals as well as insects, but consistent population cycling has rarely been documented in agroecosystems and never for a beetle. We analysed the long-term population patterns of the cabbage stem flea beetle Psylliodes chrysocephala in winter oilseed rape over 50 years. Psylliodes chrysocephala larval density from 3045 winter oilseed rape fields in southern Sweden showed strong 8-year population cycles in regional mean density. Fluctuations in larval density were synchronous over time across five subregional populations. Subregional mean environmental variables explained 90.6% of the synchrony in P. chrysocephala populations at the 7-11year time-scale. The number of days below -10°C showed strong anti-phase coherence with larval densities in the 7-11year time-scale, such that more cold days resulted in low larval densities. High levels of the North Atlantic Oscillation weather system are coherent and anti-phase with cold weather in Scania, Sweden. At the field-scale, later crop planting date and more cold winter days were associated with decreased overwintering larval density. Warmer autumn temperatures, resulting in greater larval accumulated degree days early in the season, increased overwintering larval density. Despite variation in environmental conditions and crop management, 8-yearcycles persisted for cabbage stem flea beetle throughout the 50 years of data collection. Moran effects, influenced by the North Atlantic Oscillation weather patterns, are the primary drivers of this cycle and synchronicity. Insect pest data collected in commercial agriculture fields is an abundant source of long-term data. We show that an agricultural pest can have the same periodic population cycles observed in perennial and unmanaged ecosystems. This unexpected finding has implications for sustainable pest management in agriculture and shows the value of long-term pest monitoring projects as an additional source of time-series data to untangle the drivers of population cycles.

Trends, impacts, and local responses to drought stress in Diamare Division, Northern Cameroon

Drought is one of the climatic events that threaten water availability, food security, and local livelihoods in semi-arid areas. Whilst studies have evolved on the impacts of climate change on humanity, especially in Sub-Saharan Africa, limited scholarly insight exists on the impacts of specific climatic stressors and shocks and how residents respond to such. This study contributes to climate change policy planning debates by analysing the trends, impacts and local responses to drought drawing on household questionnaires, participatory field observations, key informant interviews, and focus group discussions. The CUSUM and Mann-Kendall tests were used to analyse weather data (rainfall and temperature) to establish their trends. The Standardized Precipitation Index (SPI) was used to determine drought occurrences and intensities. The results showed significant variation: rise and decline in temperature and rainfall over the years. The SPI (range from -0.01 to -2.8) indicated extreme, severe, moderate, and mild drought (mild (73%) being the notorious) event that instigated crop failure, drying of water sources, loss of pasture, food shortages, high prices of foodstuffs, malnutrition, water scarcity, and health problems and loss of livestock. Households responded by utilizing crop residues and tree branches as fodder for livestock, reducing the number of times households eat, and changing crop varieties and planting dates. This study argues that considering the geographical variation of climatic shocks and stressors, a focus on the specific climatic event may offer the possibility for an in-depth understanding of its impacts on local residents, and useful for engendering climate planning policy intervention in Sub-Saharan Africa.

Smallholder farmers’ perceptions and adaptation strategies to climate change risks in northwest Ethiopia

PurposeThe purpose of this paper was to examine smallholder farmers’ perceptions of climate change risks, adaptation responses and the links between adaptation strategies and perceived/experienced climate change risks in South Gondar, Ethiopia.Design/methodology/approachThis paper used a convergent mixed methods design, which enables us to concurrently collect quantitative and qualitative data. Survey data was collected from 352 households, stratified into Lay Gayint 138 (39%), Tach Gayint 117 (33%) and Simada district 97 (28%). A four-point Likert scale was used to produce a standardised risk perception index for 14 climate events. Moreover, using a one-way analysis of variance, statistical differences in selecting adaptation strategies between the three districts were measured. A post hoc analysis was also carried out to identify the source of the variation. The findings of this paper are supplemented by qualitative data gathered through focus group discussions and key informant interviews of households who were chosen at random.FindingsThe standardised climate change risk perception index suggests that persistent drought, delayed onset of rainfall, early termination of rainfall and food insecurity were the major potentially dangerous climate change risks perceived by households in the study area. In response to climate change risks, households used several adaptation strategies such as adjusting crop planting dates, crop diversification, terracing, tree planting, cultivating drought-tolerant crop varieties and off-farm activities. A Tukey’s post hoc test revealed a significant difference in off-farm activities, crop diversification and planting drought-tolerant crop types among the adaptation strategies in the study area between Lay Gayint and Simada districts (p < 0.05). This difference reconfirms that adaptation strategies are location-specific.Originality/valueAlthough many studies are available on coping and adaptation strategies to climate change, this paper is one of the few studies focusing on the linkages between climate change risk perceptions and adaptation responses of households in the study area. The findings of this paper could be helpful for policymakers and development practitioners in designing locally specific, actual adaptation options that shape adaptation to recent and future climate change risks.

The possibilities of explicit Striga (Striga asiatica) risk monitoring using phenometric, edaphic, and climatic variables, demonstrated for Malawi and Zambia.

Food insecurity continues to affect more than two-thirds of the population in sub-Saharan Africa (SSA), particularly those depending on rain-fed agriculture. Striga, a parasitic weed, has caused yield losses of cereal crops, immensely affecting smallholder farmers in SSA. Although earlier studies have established that Striga is a constraint to crop production, there is little information on the spatial extent of spread and infestation severity of the weed in some SSA countries like Malawi and Zambia. This study aimed to use remotely sensed vegetation phenological (n = 11), climatic (n = 3), and soil (n = 4) variables to develop a data-driven ecological niche model to estimate Striga (Striga asiatica) spatial distribution patterns over Malawi and Zambia, respectively. Vegetation phenological variables were calculated from 250-m enhanced vegetation index (EVI) timeline data, spanning 2013 to 2016. A multicollinearity test was performed on all 18 predictor variables using the variance inflation factor (VIF) and Pearson's correlationapproach. From the initial 18 variables, 12 non-correlated predictor variables were selected to predict Striga risk zones over the two focus countries. The variable "start of the season" (start of the rainy season) showed the highest model relevance, contributing 26.8% and 37.9% to Striga risk models for Malawi and Zambia, respectively. This indicates that the crop planting date influences the occurrence and the level of Striga infestation. The resultant occurrence maps revealed interesting spatial patterns; while a very high Striga occurrence was predicted for central Malawi and eastern Zambia (mono-cultural maize growing areas), lower occurrence rates were found in the northern regions. Our study shows the possibilities of integrating various ecological factors with a better spatial and temporal resolution for operational and explicit monitoring of Striga-affected areas in SSA. The explicit identification of Striga "hotspot" areas is crucial for effectively informing intervention activities on the ground.

How agricultural droughts are contributing to child undernutrition in sub-Saharan Africa

Countries in sub-Saharan Africa (SSA) have some of the highest levels of child malnutrition, with more than one third of children under five in the region characterized as chronically undernourished. High reliance on subsistence farming, poor adoption of irrigation technologies and variable climate conditions make populations in SSA highly vulnerable to malnutrition during droughts. We use anthropometric data for 570,540 children under the age of five from 34 countries in SSA in combination with high resolution agricultural and climate data to estimate the association between agricultural droughts and child undernutrition in the region. We use global gridded data on the geographical distribution of crop areas for 19 major crops. Data on crop planting and harvesting dates are also collected for each crop. The Standardized Precipitation Evapotranspiration Index (SPEI), a multi-scalar drought index, is used to measure the intensity and spatial distribution of droughts during key periods of agricultural production (planting, growth and harvesting) and of different duration (seasonal and long-lasting droughts). Our preliminary analysis shows that droughts during the main crop growing season are associated with increased risk of child undernutrition in SSA. As a next step, we will quantify the impact of droughts during the planting and harvesting seasons and for specific crops. We will additionally investigate whether droughts impact pastoral communities, for example through livestock loss. The findings presented in this study call for urgent action to improve drought monitoring and response in SSA where the risks to child health posed by global warming are considerable. Under climate change, the severity and frequency of extreme weather and climate events, including droughts, are projected to increase in the future, which will place millions of children at risk of hunger unless timely actions plans are taken to improve food security in the region. Key words: drought, undernutrition, agriculture, sub-Saharan Africa

Agronomic cover crop management supports weed suppression and competition in California orchards

AbstractCover crops enhance the biodiversity of cropping systems and can support a variety of useful ecosystem services, including weed suppression. In California orchards, cover crops are typically implemented as annual plants that can replace resident vegetation in orchard alleyways during the rainy winter season. Our research objective was to evaluate cover crop management factors that support a competitive, weed-suppressing cover crop in the unique orchard systems of central California. We conducted two experiments: an experiment evaluating cover crop management intensification in walnuts (Juglans regia L.) and an experiment evaluating multispecies cover crop mixes and planting date in almonds [Prunus dulcis (Mill.) D.A. Webb]. These experiments demonstrate that timely cover crop planting is important for producing an abundant cover crop, and a variety of cover crop management programs can produce weed-suppressing cover crops. However, cover crops do not result in weed-free orchards and should be considered within the context of integrated management programs. The apparent flexibility of orchard cover crop management provides an opportunity to promote other agroecosystem services, with vegetation management and weed suppression as complementary management goals.

Quantifying the germination response of Parthenium hysterophorus at various temperatures and water potentials by using population-based threshold model.

Predicting the germination behavior of parthenium weed against different conditions of temperature and osmotic stress is helpful for studying the growth and development history of parthenium in different ecological contexts. Sustainable weed control strategies based on population-based threshold (PBT) models are profitable tools for crop planting date, herbicide application, and tillage operation time. To predict the emergence of parthenium by using thermal time (TT), hydrotime (HT), and hydrothermal time (HTT) analyses, seeds were exposed to varying constant temperatures (5, 10, 15, 20, 25, 30, 35, and 40°C) and water potentials (− 0.25, − 0.5, − 0.75, and − 1.0 MPa) under a controlled environment. Parthenium seeds showed better responses in terms of higher germination percentage and lower germination time at 20 and 25°C. The use of the germination modeling approach proposed the base temperature (7.2°C), optimum temperature (20°C), and ceiling temperature (42.8°C) for this weed. Moreover, germination behavior was also studied at different water potentials under different temperature regimes (10, 20, and 30°C). The HTT model predicted higher germination percentages (82.8 and 54.8%) of parthenium seeds at water potentials from 0 to −0.25 MPa, respectively, under a temperature of 20°C, and also identified a base water potential (Ψb(50) of − 0.54 MPa for germination. In conclusion, the use of the HTT modeling approach is helpful for predicting the emergence response of parthenium in a changing climate and ultimately supportive in time scheduling of parthenium weed management in cropping systems.