Fertilizer Management

The effect of in vitro plant density and mineral concentrations on subsequent
greenhouse growth was examined using a response surface method. In vitro plants
were propagated in 35 different treatments of nutrient elements P (1.25-6.25 mM),
Ca (3-9 mM), Mg (1.5-4.5 mM), KNO3 (20-60 mM) and plant density factors (6-18
buds per vessel), selected by D-optimal criteria and cultured over 5-months in
intermittent-immersion rocker bioreactors using mineral nutrients plus sucrose fedbatch
technique. Plants were transferred to greenhouse and grown for 6-months.
Plants grown in high density (18 buds/vessel) in vitro culture produced more new
plants, had the highest relative fresh growth and the largest rhizome dry mass
compared to low density (6 buds/vessel) with similar concentrations of mineral in
vitro. The plants grown in vitro with moderate KNO3 (37.7 mM) produced 8±0.9 new
plants/stock plant in greenhouse. Moderate P (3.5-4 mM) and Ca (5.7-6 mM), lowest
Mg (1.5 mM), and highest KNO3 (60 mM) showed greatest mass increase (26.6±2.3
fold/stock plant) and greatest rhizome production (50.7±2.8 g dry mass/stock plant)
in greenhouse. Secondary rhizomes were observed in all treatments. A multifactor
design in vitro resolved combined effects of plant density and several elements
during subsequent growth in one greenhouse experiment. High in vitro plant density
combined with proper mineral nutrition yielded more plants that continue to grow
better during a season of greenhouse growth. Optimization for propagation stock
has a different nutrient mixture than optimization for biomass and rhizomes.

Wetland rice (Oryza sativa L.) production contributes 55% of agricultural greenhouse gas (GHG) emissions in the world. Hence any new technology with the potential to reduce the GHG emissions of wetland rice could make a significant contribution to total global warming mitigation by agriculture. We applied a streamlined life cycle assessment to the effect of a novel unpuddled transplanting of rice and of increased crop residue retention on GHG emissions from rice fields in the Eastern Gangetic Plains. We compared them with the conventional puddling of soils and current residue retention for transplanting. The GHG emissions from one tonne of rice production for the following four cropping practices were studied: a) conventional puddled transplanting with low residue retention (CTLR); b) conventional puddled transplanting with high residue retention (CTHR); c) unpuddled transplanting following strip tillage with low residue retention (UTLR) and; d) unpuddled transplanting with high residue retention (UTHR). The emissions recorded on-farm and emissions related to pre-farm activities were converted to CO2-eq using Global Warming Potential (GWP) values of GHGs for 20-, 100- and 500-year time horizons. The GHG emissions of 1 tonne of rice varied from 1.11 to 1.57 tonne CO2-eq in the 100-year horizon. For all four treatments, soil methane (CH4) was the predominant GHG emitted (comprising 60–67% of the total) followed by emission from on-farm machinery use. The UTLR was the most effective GHG mitigation option (it avoided 29%, 16% and 6% of the total GHG emissions in comparison with CTHR, CTLR and UTHR, respectively) in wetland rice production. The novel minimum tillage establishment approach for rice involving strip tillage followed by UT has potential to increase global warming mitigation of wetland rice in the Eastern Gangetic Plains, but further research is needed to assess the role of increased residue retention.

A  field  experiment  was  conducted  at  Cotton  Research  Station,  Nanded  (M.S.)  India  during kharif season of 2019 and 2020 to study effect of moisture conservation practices and fertilizer levels on earliness of Bt cotton under rainfed condition. The trial was conducted with Bt cotton hybrid NHH 44 (BG II) in split plot design and was replicated thrice. Four moisture conservation treatments (M1-flat bed, M2-opening of furrow at 60 DAS, M3-application of superabsorbent @ 12.5 kg ha-1and M4-broad bed and furrow) were evaluated in main plot along with four NPK levels (F1-80:40:40 kg ha-1, F2 -100:50:50 kg ha-1, F3 -120:60:60 kg ha-1and F4 -140:70:70 kg ha-1) in sub plot. The broad bed and  furrow  (BBF) practice  of  moisture  conservation  was  found  to  have  significantly  higher  seed cotton  yield (1369 kg ha-1) and per cent share to the total seed cotton  yield  was 58.58 per cent in first picking over control. However, opening of furrow recorded more seed cotton yield (1125 kg ha-1)  and  its  share  to  total  yield  was  47.97  per  cent  at  second  picking.  Seed  cotton  yield  in  second picking was significantly increased in moisture conservation practices. The BBF was found to have earliness with respect to various indices (Bartlett’s index, combined picking and day index, fraction of  first  picking  index,  mean  maturity  date  index  and  production  rate  index)  followed  by  other moisture conservation practices over flat bed.
Fertilizer levels didn’t affect seed cotton yield in first  picking.  Higher  fertilizer  levels  of  F3 and  F4 being  significant  over  lower  levels  for  seed  cotton  yield  in  second  picking;  showed  delayed  crop maturity based on various earliness indices.

Keskin M, SM Say, YE Sekerli, M Sehri. 2021. Appraisal of Three Proximal Sensing Systems to Estimate Macronutrient Contents of Detached Soybean Leaves, Communications in Soil Science and Plant Analysis, DOI: 10.1080/00103624.2021.1900862. ...... ...... ....... ......... ........ ......... ........ ....... ....... ....... ....... ...... .........  ABSTRACT:  Overapplication of fertilizers is common and may lead to plant toxicity and pollution of water resources in agriculture. A practical method is needed to estimate nutrient stress quickly, easily, and economically for a sustainable management. This study dealt with the prediction of leaf moisture content (MC) and macronutrient contents including nitrogen (N), phosphorus (P), and potassium (K) of soybean leaves using three different optical instruments of chromameter, chlorophyll meter and fluorometer. Forty-five leaf samples from a commercial soybean field were obtained. Color parameters (L*, a*, b*), SPAD (soil plant analysis development) values and quantum yield (QY) values were quantified for each leaf sample. Leaf and soil samples were analyzed using standard leaf and soil chemical analysis procedures. Correlation analysis, principal component analysis (PCA) and partial least square regression (PLSR) were used for data analysis. High correlation was found between two color parameters (L* and b*) with MC and macronutrient contents (r ≥ 0.65) and also, among SPAD, QY, MC, and macronutrient contents (r ≥ 0.70). The prediction models were evaluated based on the RMSEP (root mean square error of prediction) and R2 (coefficient of determination) values.  The results showed that soybean leaf N content can be estimated using all three optical instruments with chromameter giving slightly better results (RMSEP = 0.23%, R2 = 0.80). Concerning the MC, chlorophyll meter gave slightly better prediction performance (RMSEP = 2.69%, R2 = 0.73) than the other two instruments. Hence, results suggest that these three optical instruments can be used to assess the macronutrient contents of soybean leaves quickly, easily, and economically. ...... ...... ....... ......... ........ ......... ........ ....... ....... ....... ....... ...... ......... ÖZET: Tarımda aşırı kimyasal gübre kullanımı yaygın bir uygulama olup bitki zehirlenmelerine ve su kaynaklarının kirlenmesine yol açabilir. Sürdürülebilir bir tarımsal yönetim açısından besin elementi stresini hızlı, kolay ve ekonomik bir şekilde tahmin etmek için pratik bir yönteme ihtiyaç bulunmaktadır. Bu çalışma, üç farklı optik cihazın (renk ölçer, klorofil metre ve florometre) soya bitkisi yapraklarının azot (N), fosfor (P), potasyum (K) ve nem içeriğinin tahminindeki performansını değerlendirmek için yapılmıştır. Çalışmada ticari bir soya tarlasından renk bakımından farklılık gösteren 45 yaprak örneği alınmıştır. Yaprak örnekleri için renk parametreleri (L*, a*, b*), SPAD ve kuantum verimi (QY) değerleri ölçülmüştür. Yaprak ve toprak örnekleri, standart yaprak ve toprak kimyasal analiz yöntemleri ile analiz edilmiştir. Veri analizi için korelasyon analizi, temel bileşen analizi (PCA) ve kısmi en küçük kareler regresyon (PLSR) yöntemleri kullanılmıştır. Nem içeriği ve makro besin içerikleri (r≥0.65) ile iki renk parametresi (L* ve b*) arasında ve ayrıca SPAD, QY, MC ve makro besin içerikleri (r≥0.70) arasında yüksek korelasyon bulunmuştur. Tahmin modelleri, RMSEP (tahmini karekök ortalama hata değeri) ve R2 (değişkenlik belirleme katsayısı) verilerine göre değerlendirilmiş. Sonuçlar, soya yaprağı N içeriğinin, renk ölçer biraz daha iyi performansla olmak üzere üç optik cihaz ile tahmin edilebileceğini göstermiştir (RMSEP=%0.23, R2=0.80). Nem içeriği ile ilgili olarak, klorofil ölçer diğer iki cihazdan biraz daha iyi tahmin performansı (RMSEP=%2.69, R2=0.73) göstermiştir. Sonuçlar, bu üç optik aletin soya bitkisi yapraklarının makro besin içeriğini hızlı, kolay ve ekonomik bir şekilde değerlendirmek için kullanılabileceğini göstermiştir.

Optimizing grain production implies defining the best management practices for a set of particular environments. Argentinean farmers in the central temperate region are sowing maize at two contrasting sowing dates (September to October and December), exposing their crops to very different growing environments. We tested the influence of management and environmental variables affecting maize yield at early (ES) or late (LS) sowings. Our objectives were to (i) determine the most relevant management and environmental yield predictors for ES and LS, (ii) quantify the magnitude of their effects, and (iii) explore potential yield increments after optimizing crop management within each sowing. We conducted 91 on-farm multi-environment trials during six years (2010-2016) around the central temperate region, and analyzed 13 management and environmental variables. The most relevant environmental predictors (relative importance > 0.50) for both sowing dates included presence of a water table at sowing, rainfall during the crop cycle, and their interactions. Presence of a water table had a positive or negative effect for ES or LS, respectively. Management yield predictors varied depending on the sowing date. Stand density, N and S availability were important yield predictors at ES, while fungicide use, soil P, and N availability were the most relevant ones at LS. Farmers can increase yield at each sowing date by optimizing these management practices. Optimizing stand density and N availability in ES can have a ∼3,053 kg ha −1 effect, while fungicide use in LS can increase yield by ∼1040 kg ha −1. Determining the adequate sowing date based on the presence of a water table at sowing can have a ∼1000 kg ha −1 effect. Our results described specific management options for reducing yield gaps and optimize maize production across contrasting sowing dates.